Materials science and engineering of phase change

Materials Science and Technology

ISSN: 0267-0836 (Print) 1743-2847 (Online) Journal homepage: http://www.tandfonline.com/loi/ymst20

Materials science and engineering of phase change random access memory Syed Ghazi Sarwat To cite this article: Syed Ghazi Sarwat (2017) Materials science and engineering of phase change random access memory, Materials Science and Technology, 33:16, 1890-1906, DOI: 10.1080/02670836.2017.1341723 To link to this article: https://doi.org/10.1080/02670836.2017.1341723

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Date: 02 December 2017, At: 04:54

MATERIALS SCIENCE AND TECHNOLOGY, 2017 VOL. 33, NO. 16, 1890–1906 https://doi.org/10.1080/02670836.2017.1341723

Materials science and engineering of phase change random access memory Syed Ghazi Sarwat

Downloaded by [192.76.8.75] at 04:54 02 December 2017

Department of Materials, University of Oxford, Oxford, UK ABSTRACT

ARTICLE HISTORY

Having monopolised the optical data storage industry since the very beginning, phase change materials are now being intensively explored for next-generation electronic data storage, referred to as phase change random access memory (PCRAM). Because phase change materials are electrically programmable; capable of reversibly switching between two stable structural phases of contrasting electrical properties, besides data storage they also enable data computation. For these reasons, PCRAM envisages to overcome both miniaturisation and data flow bottlenecks, challenges which current silicon charge-based technology is failing to cope with. This review, while reasoning the need for a switch to a newer data storage technology, and comparing PCRAM with other data storage and computation platforms, comprehensively takes stock of the benefits and challenges associated with PCRAM. This review also critically investigates and associates the materials science and physics, such as the atomic structure and bonding, thermodynamics and kinetics of the phase transformation, with the PCRAM device characteristics and performance. Various device design-concepts and requirements are reviewed. Recent advances, and evolution of newer platforms, including those relating to neuromorphic computing and photonic memory are also described.

Received 12 December 2016 Revised 4 May 2017 Accepted 17 May 2017 KEYWORDS

Phase change materials; phase change memory; transistors; thermodynamics

This is the winning review of the 2017 Materials Literature Review Prize of the Institute of Materials, Minerals and Mining, run by the Editorial Board of MST. Sponsorship of the prize by TWI Ltd is gratefully acknowledged

Introduction Data storage technology The extent to which data computation and storage platforms like computers, mobile phones, MP3 players etc. have pervaded our lives is truly remarkable. These machines work on silicon-transistor-based integrated circuits, which deploy electric charges to process and store data in the form of binary numbers (0 and 1). Research on this charge-based technology occupies a unique position in academia and industry, both working towards improving the data processing speeds and storage capacities. Over the years it has become quite clear that these characteristics are best achieved through device miniaturisation, which is scaling down the dimensions of the functional units, such as the transistors, so that their number, hence density on a single package is increased. The Moore’s law [1] describes this scaling behaviour: it predicts doubling of transistors on an integrated circuit every 18 months, and engineers world-wide have worked furiously to maintain it thus far, since it was first proposed in 1965 (a stateof-the-art chip as of today carries as many as 7 Billion transistors). However, for the first time, chip making industries are uncertain about further miniaturisation. It has been convincingly shown [1,3] and proven multiple times in the International Technology Roadmap CONTACT Syed Ghazi Sarwat

[email protected]

© 2017 Institute of Materials, Minerals and Mining.

for Semiconductors that the current technology will not be commensurate with future computation and storage requirements. This has to primarily do with the fabrication costs and irresolvable atomic scale challenges which will arise from further miniaturisation. In addition to this, another challenge in enhancing the computer’s performance will be to tackle the von-Neumann bottleneck [2]. In the current platforms, electronic data need to be shuttled between memories and the processor each time an application is opened or a file is saved. Memories and processor are units which in current computer architecture are placed away from each other. Thus, the bottleneck is the speed of data transmission. So, even with the fastest processors imaginable, processing would be limited by the data flow. For these reasons, alternate data storage and computation technologies are being actively explored. The ideal platform is considered to be one that singlehandedly counters both miniaturisation issues and the von-Neumann bottleneck. Such a platform could very well be achieved through co-location of memory and processor, which would facilitate data storage and computation within the same space in the computer. Colocation can best be achieved through development of novel non-volatile memories [3–5], of kinds much different from the contemporary charge-based Flash


MATERIALS SCIENCE AND TECHNOLOGY

and magnetic hard disks. This class of new memories include ferroelectric RAM [6], magnetoresistive RAM (MRAM) [7], racetrack memory [2], spin-torque transfer RAM [8], organic memory [9], resistance RAM [10], phase change memory [11], carbon nanotube RAM [12], quantum memory [13], and solid electrolyte memory [14]. Amongst these, magnetic and ferroelectric RAM have already found their place in the market, while others are at different stages of research, and approval. However, owing to their bigger unit size, these memories tend to lack high storage densities, which is their major downside. To this end, phase change memory (PCRAM) or the ovonic unified memory/storage class memory has garnered much attention as a potential next-generation data storage and computation technology. In this review, PCRAM is compared with other memory platforms, and the material science, physics and geometrical characteristics of PCRAM devices are comprehensively surveyed. As recent research has brought to light new possibilities, and revealed previously latent weaknesses, this article will also review modern advances. Due to high interest in this field, several other reviews on different aspects of PCRAM have been made. This review is therefore written slightly differently, intended to capture important concepts, so as give a holistic picture of PCRAM to new readers, which previous reviews have missed, and refresh and update those familiar with it through discussions on recent advances. Limitations of silicon technology Device miniaturisation is generally described by the Dennard scaling rules. Scaling transistors had progressed non-stop and in agreement with the scaling rules until the 90 nm node (channel length) was achieved [1]. Today, we are at 14 nm node technology. However, for various pressing reasons [15] such as design complexities, increased power consumption and heat dissipation, increased cost, quantum effects, hot electron degradation etc. further miniaturisation has become a formidable task. The first three in this list represent the macroscopic challenges. For instance, tasks of accommodating more wirings, minimising power consumption and densities, and over-heating. In quantum effects, gate thickness, and/or channel length reduction encourage electrons tunnelling, resulting in charge leakages, and/or subthreshold switching [16]. Devices at reduced dimensions also experience hot electron degradation [17], where enhanced local electric fields make electrons sufficiently energetic, capable of changing local chemical stoichiometry through breaking chemical bonds within the channel, oxide and their interface. Phase change memory Phase change memory gets its name from phase change materials. Stanford Ovshinsky is often acclaimed as

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the father of phase change memory, for his work of 1968 [18,19] that first demonstrated electric field driven resistivity transitions in chalcogenides. Interestingly, it is less known that the discovery of phase change material could have dated much earlier, starting from 1900 [18], had those reports not considered this as an elusive behaviour. As the name implies, this technology encodes data using the material’s phases, unlike the silicon-based technology, where electrical charges are used. Phase change materials can be electrically programmed, i.e. they can be switched reversibly between two or more structural phases. Such transitions occur in a few nanoseconds, and these states have significantly distinct electrical and optical properties. For example, the electrical resistance ratio [20] between the amorphous and the crystalline phase can be of order ∼ 2000. Information (bits) in a phase change material can therefore be stored as a function of its phase [11], for example: 1 for crystalline (low resistance), and 0 for amorphous (high resistance). In practice, the switching operation can be facilitated by either or combination of thermal, optical or electrical (Joule Heating) stimuli [21]. The nomenclature commonly adopted has been illustrated pictorially in Figure 1(A). In an electrical sense, the SET step defines the electrical power required for transforming the amorphous PCRAM material into its crystalline form, while the RESET step indicates the reverse transformation, i.e. amorphous to crystalline. In the SET stage, it is thought that the stimuli (Joule heating) heats the material above its glass transition temperature (T g ) [22], into a conductive crystalline state. Whereas, in the RESET stage, the electrical pulses take the material beyond its melting point (T m ) [21], and subsequent quenching produces an amorphous volume that has a greater resistivity. However, it is argued that RESET can also occur without any solid–liquid transformation. In GST nanowires, it is demonstrated to be a result of accumulation of dislocations, which produces a local disordered (amorphous) region [23]. Dislocations mobilise before they accumulate due to the electron wind forces produced from applied electric fields. In interfacial phase change memory, RESET is understood to be from directional migration of selective atoms [24] from bulk of a layer to the interfaces in a superlattice structure. It is indeed surprising that despite these uncertainties in the material’s behaviour, phase change materials remain successfully commercialised for optical (CD/DVD) data storage, and most regarded, although less successfully commercialised, for electronic data storage. An efficient PCRAM device is one which switches at minimum electrical power. In particular the RESET stage is demanding in energy consumption, as high temperatures are required to melt the material. Figure 1(B) shows typical current–voltage characteristics of

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In this section, an attempt is made to describe these technologies, while collating them with PCRAM.

Figure 1. Illustration showing data storage protocol in PCRAM: (A) schematic of SET and RESET voltage pulses and (B) artistic representation of a typical current–voltage behaviour in crystalline (low resistance) and amorphous (high resistance) states. In a PCRAM device, Joule heating regulates temperature through the phase change material, and hence phase transformations. Amorphous to crystalline transition occurs at V TH , called the threshold bias. Inset is a graph illustrating temperature-dependent resistivity of a sputtered-deposited amorphous Ge1 Sb2 Te4 (phase change material) thin film. Arrows indicate phase transformations. At 150° C, indicated by a substantial dip in resistivity, the amorphous phase transforms into a crystalline phase of a rocksalt structure. At 200° C, a second subtle resistivity dip occurs, which corresponds to transformation of the rocksalt structure into a trigonal structure (reprinted with the permission from Ielmini and Lacaita [26], © Elsevier 2008).

a phase change material-based device. The device switches from a highly resistance state (amorphous) into a less resistive crystalline state at a bias called the threshold voltage (V TH ). This is the SET stage. For RESET, the crystalline state is melted and quenched rapidly. READ, which is retrieval of the device state, is done at a bias lesser than V TH to avoid possible crystallisation events. Interestingly, although phase change materials were reported in 1968, it was the disclosure of nanosecond timescale switching behaviour in 1987 [25], which fascinated the scientific community. Today, a plethora of research could be found on phase change materials and memory [11,22,26–28], the majority of which being on the quest and optimisation of materials, and cell’s geometry. Current and future technologies Before the phase change memory technology is detailed, it is required that readers become acquainted with alternate technologies relating to memory and computation.

Dynamic RAM. Dynamic random access memory (DRAM) is the largest store house of temporary data. A typical DRAM cell consists of a transistor, and a capacitor. The transistor controls reading, and writing of data, while capacitor functions to store it. Capacitors are prone to leakage, particularly when miniaturised to nano-dimensions. This makes them energy intensive since regular refreshments are required to retain data [29]. Further, cyclic recharging makes DRAM slower, because before each read, a pseudo write has to be carried-out. But, for their simple cell structure, they occupy less space. In sharp contrast, a PCRAM cell stores information as a material phase; the data is thus non-volatile, which eliminates the need for cyclic recharge. Further, modern PCRAM cells are faster and can be lithographed to sub-10 nm [28,29] dimensions (4F2 , where F is the minimum possible feature size through lithography), thus providing much greater storage capacity. Static RAM. Unlike DRAM, static random access memory (SRAM) undermines the need for cyclic charging by eliminating the need for capacitors: this makes them ultra-fast. A typical SRAM cell consists of flip-flops, constructed from four to six transistors [26]. However, since their cell structure is complicated, they tend to occupy much larger space (140F 2 ). This makes SRAM much more expensive than DRAM. SRAM are therefore used as cache memories, where write/erase time is critical (0.3 ns). Commercial PCRAM cell for its simple structure, and miniaturisation capability, are more attractive than SRAM but limited by the access speeds (50 ns); although switching speeds of order 500 ps have been demonstrated in research labs [30]. Flash. Flash by far is the most appreciated non-volatile memory [4]. The operation speed of Flash is much slower than DRAM, and hence they are used in secondary data storage packages like USB-thumb drives. It works on an entirely different transistor-type, which enables data retention even after power is turned-off. Unlike typical transistors, flash employs two different gates: control and floating, separated from the channel by a dielectric, typically oxides. Flash technology comes in two forms: NOR (cells arranged on parallel) and NAND (cells arranged in series); and can store more than two bits (0 and 1), by modulating the charge stored on the floating gate. Since charges are stored on the floating gate, thinning of the parting oxide layer in process of down-scaling the cell size can spur tunnelling of electrons, resulting in volatility of data. Data storage is carried-out by moving charges from channel into floating gate. This requires high bias inputs, and hence Flash is not energy efficient. Further, cyclic



writing/erasing deteriorates the oxide layer [26]. Thus, although flash proffers multiple advantages, it lacks endurance. Also Flash is much slower, with write/erase times in the order of milliseconds. In a PCRAM system, charges are replaced with phases which can remain robust indefinitely, and the power consumption is much lower than Flash, making them more energy efficient. Ferroelectric RAM. The concept of ferroelectric RAM [6,31] is based on electrical field induced switching between polarised states of ferroelectric materials, such as PZT (lead zirconate titanate). A typical ferroelectric RAM (FRAM) cell consists of a ferroelectric material sandwiched between plates of a capacitor. The direction of the applied electric field toggles the material’s polarisation direction, allowing it to store bits. FRAM has proven to show extensive endurance limit, low energy consumption and fast accessing speeds, each vouching for its applicability. However, FRAM suffers from the same downsides of both DRAM and SRAM. Like DRAM, since capacitors are used, down-scaling of dimension sets a negative impact. Further, each cell occupies more space (22F 2 ), thus like SRAM, the storage capacity is limited. Magnetoresistive RAM. The data in this memory type is stored by magnetic elements [32,33]. A MRAM cell consists of ferromagnetic plates separated by an insulating layer. The bottom pinning layer is a permanent magnet fixed in terms of direction of magnetisation, while the top plate adjusts its direction with respect to the external field, thus storing bits. The bit’s low resistance, or alternatively high resistance is governed by the magnetising direction: parallel or antiparallel, of the free plate with respect to the pinning plate. MRAM, like FRAM shows indefinite cyclability, impressive programming speeds at minimal power consumption. But its sophisticated design, requirement of sufficiently large space per cell (20F 2 ) and high write currents hampers the device density. Also of concern is the ON/OFF ratio, and energy dissipation issues. These limitations of MRAM are overcome by a variant technology called the spin transfer torque MRAM (STT-MRAM) [8]. STTMRAM works on the principle that spin polarised currents hold the ability to transfer spin angular momentum to magnetic moments of a ferromagnet, thus able to re-orient them. Thus, since electrical currents are used in writing bits, between a fixed and movable magnet which are separated by a barrier layer, STT-MRAM can be very well scaled down. STT-MRAM are close to commercialisation, and are a leading competition for PCRAM, particularly because they are fast, being the only current non-volatile memory capable of replacing SRAM. However, current STT-MRAM platforms require relatively large write currents, undergo breakdown of the barrier layer, and their low resistance ratio

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requires a memory cell architecture that limits its device density. Resistive oxide memory. Many transition metal oxides, normally insulating, can be made conductive through application of electric fields [34,35]. Conduction occurs through a filamentation process, which could be vacancy or metal ion migration. The original resistive state can be retrieved by disconnecting the filaments from the electrodes through Joule heating. Multilevel data storage has been demonstrated with resistive oxide memory (RRAM), which is a function of filaments size and spacing. RRAM is fabrication-friendly and free from miniaturisation issues (4F 2 ) as it is not charge based. This technology is both energy efficient and fast, and is at the forefront for non-volatile data storage. Particularly, conductive-bridging RAM (CBRAM), which is based on metallic filaments, is the biggest competitor for phase change memory. However, it is now becoming clear that low-power switching [36] of RRAM necessarily involves moving only a countable number of atoms. This introduces very large intra-device variability, making it difficult to achieve control [37]. Thus interest in filamentary RRAM is now beginning to subside among industry researchers, for applications other than low-density embedded storage [38]. If PCRAM is to be compared against STT-MRAM and RRAM/CBRAM, which are its fierce competitors for non-volatile data storage, it would fall short on process speed, and endurance limits. However, PCRAM is more matured a technology, and phase change materials are already well-studied and mass produced for optical data storage, giving PCRAM an upper hand.

Science of phase change materials Threshold voltage: a key player As previously described, switching from the high resistance amorphous state to a conductive crystalline state occurs when the material is heated beyond its glass transition temperature. Low programming currents are desired for low energy consumption, but in practice cannot provide sufficient Joule heating to facilitate phase transformations. It is a phenomenon called threshold switching [39] that essentially makes switching plausible with smaller currents. Threshold switching is not a unique feature of chalcogenides, it has also been observed in amorphous boron [40], amorphous silicon [35], transition metal oxides [41], and amorphous pnictides [26]. Threshold switching has been explained by a variety of models [39,42,43], each giving a nice fit to the experimental data [39]. However, one can also gain an insight to it from a materials science perceptive [44,45]. From this standpoint, it is defined as the external stimulus: field, and/or heat required, which converts sub-critical nuclei in the amorphous matrix into super-critical


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nuclei, which grow until they impinge each other, forming conductive crystalline pathways. Atomic force micrographs in Figure 4 show how nuclei evolve in a chalcogenide. From a physics outlook, it is best understood from voltage-current instability [18,26,39,46]. At subthreshold fields ( < V TH ), the trapped electrons, which are resultant of the short-range disorder produced defects such as dangling bonds, and/or impurities in the amorphous matrix, and placed within the bandgap mobilise owing to the gained momenta. Such high energy electrons hop between other trap states; thereby rendering net conductivity. This field-dependent conductivity is termed the Poole–Frenkel mechanism [15], and is demonstrated in Figure 2(A). Without an external field, the trapped electron needs to overcome a large barrier in order to become mobile. Application of an external field changes the shape of this barrier, consequently decreasing the barrier height and facilitating thermal emission of electrons. Conduction is dependent on the inter-trap distance; increasing with decreased distance. At and beyond the threshold field ( > V TH ), the equilibrium, indicated by the Fermi level (highest energy state occupied by the electrons) gets disturbed as is represented in Figure 2(B). Electrons well within the bulk of the material acquire an effective temperature due to field, which allows them to access shallow states near the conduction edge via thermal emission and/or tunnelling. The occupancy of the shallow states increases exponentially with increase in the applied voltage due to the finite relaxation times. This results in a proportionate exponential increase in the current. Therefore, intuitively, threshold bias decreases with: (a) increase in temperature, and/or (b) decrease in gap between fermi and conduction edges.

For materials with relatively low crystallisation temperatures, the resultant current provides sufficient Joule heating (thermal runaway) for the phase to permanently transform, whilst when the crystallisation temperature is sufficiently large, the initial amorphous state may prevail. Since, thermal energy also assist the hopping of trapped electrons, the threshold voltage is observed to be temperature dependent; decreasing with increasing temperature of the solid [26,39,47]. The phenomenon of threshold switching is reversible, i.e. if the stimulus snaps back before some critical time, the resistance increases, returning to its starting value. While for the crystallisation model, this is thought to occur due to the dissolution of premature nuclei, in the electron band structure, it corresponds to re-trapping of the excited electrons. Figure 1(B) describes the characteristic S-shaped I–V curve of PCRAM materials. At and just above the threshold voltage, the resistance would snap back to its original high value if the applied bias is removed. Materials for phase change memory Typically a phase change material switches between a crystalline and an amorphous phase. While the crystalline phase is easy to achieve, as it is thermodynamically favoured, achieving an amorphous structure is a challenge. It is well known that a material, if cooled at a rate greater than it critical cooling rate (Rz ) would become amorphous. Cooling rate is a material dependent property, and can vary by many orders [48] (100 to 10 [16] K s−1 ) between materials. A first step in classification is therefore to sort-out materials, which have practicable Rz . In the second stage of investigation, the electronic and optical properties are the judging criteria. Sufficient contrast in these properties between

Figure 2. Illustration explaining the threshold switching phenomenon: (A) profile of trap state potentials under the influence of an applied electric field, whose magnitude increases from a to c. Note the downwards sloping behaviour with increase in the field strength, which is representative of barrier lowering that enables Poole–Frenkel conduction. This field-dependent barrier height reduction is responsible for sub-threshold current, and (B) electrons energy distribution in an amorphous phase change material in the less-resistive (OFF) and high-resistive (ON) states. OFF state is representative of an equilibrium state. In the ON state, the equilibrium is disturbed due to the external electrical field, and electrons begin to occupy the shallow trap states, which are near the conduction band edge. This results to electrical conductance (reprinted with the permission from Ielmini and Zhang [39], © American Physics Institute).


the amorphous and crystalline phase is required. In the last and final stage of material selection, following characteristics are looked for in the material [49].


Crystallisation rate The writing speed of PCRAM is a negative function of the material’s crystallisation rate. The faster the rate at which amorphous bits crystallise, the faster is the data storage and computation process. A material which phase transforms swiftly must therefore be ideal for PCRAM. A material’s crystallisation rate can be derived from its time dependent optical response [45] (change in reflectance) and/or electrical response [22,47] (change in resistance). Archival stability (amorphous phase stability) Because data needs to be stored indefinitely in PCRAM, it is desired that the phase which contains the bit stays robust until otherwise required to change. Since the phase of material strongly depends on temperature, materials with high T g are most desired. However, this behaviour naturally contradicts fast crystallisation rate. Therefore, a trade-off between these characteristics is generally made. The magnitude of archival stability is often determined using the classic Arrhenius extrapolation [49]. Endurance limit A PCRAM cell is expected to endure unlimited number of switching cycles. A good cell must exhibit minimum variations in properties after cyclic loadings, this being the metric for endurance limit. Endurance limit and the

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above described characteristics greatly narrows down the materials that could befit PCRAM applications. The first material [19] to have revealed phase change effect on electrical stimulus was Ti48 As30 Si12 Ge10. Since then many new materials have been reported. Interestingly, the chalcogenides: particularly, combinations of tellurium (Te) with germanium (Ge), and antimony (Sb) have always been the centre of focus. The discovery of Ge40 Sb20 Te40 and Ge22 Sb22 Te55 (GST) alloys which exhibit switching speeds < 50 ns could be regarded as the seed for the interest in chalcogenide glasses [18,22]. As of today, the most widely studied systems lie on or in the vicinity of a pseudo binary line that joins the stoichiometric compounds: Sb2 Te3 and GeTe, in the ternary phase diagram of Ge-Sb-Te, as illustrated in Figure 3(A). Among these, the alloy [18] Ge22 Sb22 Te55 (GST) has the best match of properties: fast crystallisation, amorphous phase stability, endurance limit and excellent contrast, and therefore has garnered largest attention. The rationale behind the interest in the pseudo binary line could be best explained using thermodynamics [18,50]: phase diagrams (Figure 3(B(i,ii))), and Gibbs energy vs composition plots (Figure 3(B(iii,iv))). The eutectic composition has the least driving force towards crystallisation (Figure 3B(ii)). On the contrary, the compositions that fall on the pseudo binary line form stoichiometric compounds (Figure 3(A)), which show significantly greater crystallisation tendency owing to higher driving force (Figure 3B(iv)). Furthermore, for the compositions that fall on this line, crystallisation rate is enhanced due to higher kinetic energies of the atoms, owing to high temperatures.

Figure 3. Thermodynamics basis for best PCRAM materials: (A) Ge-Te-Sb ternary phase diagram depicting various phase change alloys. Most studied compositions are on the dotted line that connects the GeTe and Sb2 Te3 compounds, and (B) (i) schematic of a binary eutectic phase diagram, (ii) composition vs Gibbs free energy plot for a temperature lesser than the melting temperature, (iii) schematic of a binary phase diagram of a compound, and (iv) composition vs. Gibbs free energy plot for a temperature lesser than the melting temperature.

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Table 1. Material properties and their influence on the device performance. Material’s characteristics


Crystallisation temperature/ thermal stability Melting temperature Resistivity contrast Thermal conductivity Crystallisation speed Melt-quenching speed Threshold voltage

Influence on the device Set power/data retention Reset power Data retention/set and reset power Set and reset power Set pulse duration/power Reset pulse duration/power Set power

Besides, for these compositions crystallisation occurs through nucleation of a single phase, unlike two for eutectic (α and β) (Figure 3(i,ii)). All these factors ease the crystallisation process, enabling fast SET, thus rapid programming. The material properties and their corresponding influence on data storage, and energy consumption are summarised in Table 1. Systems constituting Figure 3(A), and many others that belong to the chalcogenides class, are broadly classified into two types: nucleation dominated, and growth dominated alloys [44,45], depending on mechanism followed for crystallisation. Classical thermodynamics defines two conjugate stages towards phase transformation: nucleation, followed by growth. Depending upon which among these exercise dominant control over the phase transformation process, chalcogenides are differentiated into the following categories. This classification is based on the interfacial energies at the boundaries between the amorphous and crystalline phases, and amorphous/crystalline phase and the substrate/cladding layer. Nucleation dominated Crystallisation by nucleation occurs via formation of nuclei at random locations within the bulk of the phase change material [28,45]. Upon temperature rise, these nuclei grow, dilating until they impinge each other. This is when complete crystallisation occur, i.e. SET.

Examples of nucleation dominated phase change materials [18,27] include Ge22 Sb22 Te55 (GST), Ge20 Sb20 Te20 and Ge40 Sb20 Te40 . Such materials, with their nucleation rate > growth rate are fast crystallising [28], and hence undergo SET in few nanoseconds. This tendency make nucleation dominated materials the ideal candidates for electrically driven memory, since they allow maximisation of the clock frequency that drives the processor. Time required for crystallisation in the nucleation dominated phase change material depends significantly on the starting state of the amorphous volume. For instance, an amorphous phase obtained from a quenched melt allows greater crystallisation kinetics against a sputtered alloy [18,36]. This is associated with the presence of sub-critical nuclei distributed in the quenched melt, which catalyse nucleation kinetics. Presence of such quenched-in nuclei has been confirmed through fluctuation transmission electron microscopy (FTEM) studies [36]. Figure 4(A) illustrates the crystallisation behaviour of nucleation dominated materials. Growth dominated A growth dominated phase change material crystallises at growth rate > nucleation rate [27,44]. This essentially means that only a few nuclei precipitate during annealing of the amorphous volume, among which just one (single nucleus) tends to grow at the cost of others in achieving crystallisation [51]. This process is characterised by directional transformation, which is saying that a nucleus precipitate preferably at the crystalline-amorphous interface, and grows into the bulk of the amorphous volume. Thin specimens are therefore easier to recrystallise, or alternatively small amorphous marks crystallise faster than big marks. Materials which show growth dominated crystallisation include [18,27] Ag and In doped SbTe (AIST), GeSb, GeSnSb and Ge3 Sb6 Te5 . In the absence of an interface,

Figure 4. Illustration and AFM of nucleation and growth in phase change materials: (A) schematic of nucleation dominated crystallisation. Crystallites nucleate in the bulk of the phase change materials via homogeneous nucleation. On the right are atomic force micrographs (AFM) of GST. Darks regions are the crystallites and lighter is the amorphous matrix, and (B) Illustration of a growth dominated crystallisation. Here, nuclei preferentially nucleate at the interface (amorphous/oxide, amorphous/substrate, amorphous/crystalline phase) via heterogeneous nucleation and grow inwards. On the right are the atomic force micrographs (AFM) of AIST. (AFM images are reprinted with the permission from Goux et al. [36], © American Institute of Physics).



crystallisation or SET takes longer, owing to increased activation energy. Bulk growth dominated materials lag quite behind the nucleation dominated materials when it comes to the crystallisation rate. Reflectance studies [36] have shown that the crystallisation rate in AIST can be 100 times slower than GST. Doping or addition of foreign elements (dopants) is a common practice in altering the phase change material properties. Dopants are typically added in unfixed amounts, as long as the original crystal structure, and crystallisation mechanism remains unchanged. Because the dopant concentration could take large values ( > 15 at.-%) [18], the term doping is often substituted by alloying in the context of phase change materials. Dopants generally include N, O, Ga, Ge, Ag, In, Ga, and Sn [18,49,52]. Nitrogen doped alloys are a current research focus, due to ease of fabrication and superior properties [52]. Atomic bonding and structure The contrast in the properties between the crystalline and amorphous phase, particularly optical is resultant of not only the changes in atomic structure, but also in the type of the chemical bonding between the constituent atoms. It is believed, although not satisfactorily confirmed that in the amorphous phase (disordered atomic arrangement), covalent bonding dominates, where mutual sharing of electrons between neighbouring atoms occurs, while in the crystalline state a less common type of bonding, called resonance bonding occurs. In resonance bonding, the electrons are delocalised, i.e. continuously resonating between atoms. In the crystalline state of GST [18,48,52], Ge takes an octahedral coordination with Sb and Te, while in the amorphous phase it bonds with a tetrahedral coordination. The process of flipping between these coordination states is termed umbrella flip. It is understood that this is the mechanism that renders fast phase change material fast switching speed, since atoms move only by atomic distances during switching. Figure

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5(A) illustrates the umbrella flip mechanism at a unit cell level. Figure 5(B) is a consequence of umbrella flip at an atomic structure level, where long range order between is broken. However, on the contrary to several prior studies, recent experimental and simulation results have suggested that this is not strictly the case: within the amorphous phase, both six fold and four fold coordination can co-exist. The stable crystalline atomic structure is seldom achieved in practice owing to constrained kinetics. For instance, in Ge22 Sb22 Te55, devices typically switch between a distorted rocksalt structure and amorphous states; although the stable crystalline form has a hexagonal symmetry. Typically, a crystalline phase is also characterised for vacancies. For example, the Ge22 Sb22 Te55 alloy show 25% vacant sites per unit cell (reason why the stoichiometry is Ge: 22, Sb: 22 and Te: 55). Origin of vacancies in GST has been understood through quantum chemical calculations. Explanation claims that vacancies are needed in order to annihilate antibonding interaction between Ge-Te and Ge-Sb bonds, thus stabilising the crystalline phase. Additionally, unit cells with vacancies get distorted through Peierls distortion [53]. Such crystal lattice distortions are believed to further stabilise the crystalline phase [18,48], that results to faster crystallisation.

Device design Their current form may not suffice if phase change memory is to truly compete against current Si chargebased technology. Considerable amount of research has thus been put into materials compositional, and cell’s geometrical and electrical designs. In this section of the article some of the most successful ideologies are reviewed. Compositional design In this approach, properties of a phase change material are tuned through compositional and/or interface design. A case in point is doping of the SbTe alloy.

Figure 5. Schematics illustrating the atomic level changes during amorphous to crystalline (SET) and crystalline to amorphous (RESET) phase transitions in GST: (A) Ge atom is represented in red. Note the changes in the coordination number and position of Ge atom in the crystalline and amorphous states. The thicker lines represent the strong covalent bonds, and thinner lines represent the weaker bonds. Within a unit cell, during RESET, Ge atom hops from an octahedral position to a tetrahedral position across the (111) plane (reprinted with the permission from Kolobov et al. [52], © Nature 2004, and (B) unit cell level changes shown in (A) result to breaking of the long range order, which causes an ordered crystalline state to transform into a disordered amorphous state.


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The crystallisation rate of this alloy decreases, while the archival stability increases from doping: following an order [41] Ag > Ge > In > Ga > Sn. The declined crystallisation has been attributed to a fall in the mobilities of the constituent atoms. This understanding is based on higher bonding enthalpies between the dopant, and Sb, Te atoms. Nitrogen and oxygen as dopants are being actively explored in recent years. Both decrease the crystallisation kinetics [18,46,54] as they disrupt and distort the unit cells owing to atomic size mismatches. Designing by interface relies on bringing heterogeneous nucleation to use. Simpson et al. [55,56] have demonstrated how crystalline Sb2 Te3 templates can enhance the crystallisation rate in thin films. Design by interface is not a new approach. The use of this approach dates back to 1986, when Yamada et al. [57] reported fast crystallisation kinetics in (Ti80 Ge5 Sn15 )100−x Aux alloys. Using X-ray and electron diffraction studies they showed how Au acts as nucleation sites, and decrease the activation energy for crystallisation. Cell (device) design The performance of a PCRAM device is characterised not only by its switching speed, but also equally by the energy consumed towards switching. In order to boost performance, it is therefore essential to supplement the compositionally tuned phase change material, with an equally clever cell structure, one that could reduce the energy consumption. A good design considers diligent optimisation of the following parameters. Dimensions There exists a strong dependence between the threshold voltage and the physical dimensions of the phase change material [58]. This observation essentially implies that threshold voltage is a misleading ideology as it scales with the material’s physical size. Instead, threshold electric field seems more appropriate. Over the years, it has been understood that scaling down the physical volume of the phase change material reduces the switching energy, and proportionally increases the switching speed [18]. However, miniaturisation does not do well beyond some critical dimension. Several studies have observed an increase in the energy consumption on reduction of the phase change film beyond some critical thickness [55,59,60]. Critical thickness is often described for thin films: a thickness below which the energy consumption shoots. Phase transformation is found to strongly depend on the substrate material and its roughness, surrounding temperature, and the capping layer [18], these factors determining the critical thickness. Simone et al. [59] have systematically described the phase transformation in GST, NGST (Nitrogen doped), AIST, GbSe, Sb2 Te3 , and

Indium doped Sb2 Te3 , thin films (varying thickness) capped by a layer of Al2 O3 , revealing their critical thicknesses. Although the underlying science of this behaviour (increase in energy consumption on reduced thickness) remains less understood, most agreed theories are [61]: (a) interfacial interaction between the phase change material and the capping layer, and (b) mechanical stresses at the interfaces. While not much research has been dedicated in proving interfacial interactions, effects of stresses have been understood [51]. It needs to be emphasised that a material’s behaviour at reduced thickness is also a strong function of the mechanism adopted for crystallisation. Since the interface has a small role in the crystallisation of nucleation dominated materials, reduced volume is observed to decline the crystallisation rate. In sharp contrast, for the growth dominated materials, reduced volume and resultant increase in interfacial area increases the nucleation probability and growth speed [18]. Intrinsic properties of the phase change material are also observed to change upon scaling. For example, thermal conductivity is observed to decrease with film’s thickness [62]. This is a favourable change, as thermal losses and hence energy consumption are minimised. Capping/cladding layers A common strategy in minimising energy consumption is reducing the heat losses. Because the heat generated during the first switching cycle can assist subsequent cycles, minimising heat losses is understood to minimise energy consumption. For this reason, a phase change material is often encapsulated by a poor thermal conductor, termed a capping or cladding layer. A capping layer not only serves as a barrier for heat loss, but also protects the phase change material from exposure to the atmosphere. For instance, from oxidation and physical damage. Oxidation could be damaging, as it is understood to degrade the switching tendencies of phase change materials [18,45]. Furthermore, a capping layer also naturally serves as an interface- for heterogeneous nucleation. A significant research has therefore been invested in understanding and exploiting this. For example, SiO2 as a capping layer is observed to increase the activation energy required for crystallisation in GST [51] (nucleation dominated) and AIST [63] (growth dominated), while ZnS-SiO2 as a capping decreases the activation energy. Such a shift in the activation energy is argued to be a result of magnitude and type of stresses imposed on the phase change material film by the capping layers. Because phase transformation in a phase change film is assisted by a volume change, a hard capping layer increases the activation energy, and vice versa. Contact electrodes Besides the volume of the phase change material, it is also clear that a reduction in the interfacial


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contact area between the electrodes, and the phase change material can significantly decrease the energy expense for switching. This understanding follows from the increase in the contact resistance, which limits the sourced current. Higher electrical contact resistance also increases electron crowding which results to greater Joules heating. However, it needs to be emphasised increase in the contact resistance also increases the electrical power required for the read operation. Thus, a trade-off is generally made. The biggest impediment thus far in the context of reducing contact area of the bulk electrodes has been development of sub-lithographic techniques required for electrodes patterning. Resistance drift Undesired variations in the electrical conductance over time and/or due to thermal exposure [64–66] are generally observed in the amorphous phase of PCRAM devices. This phenomenon is called resistance drift, and is a big limiting factor, particularly in multilevel data storage, which as explained later is the basis of neuromorphic computing. Although no universal consensus exists to why resistance drift occurs, the most agreed theory is of structural relaxation, which causes defects annihilation. As previously described, conduction in the amorphous state is understood to be due to localised states, which are proportional to the defect concentration. Annihilation of defects, for example, through linkage of short-range networks minimises the localised trap states density [64,66,67], thus increasing the activation barrier for carriers excitation. Concomitantly, this results to increase in the device resistance. Various schemes have been proposed to minimise resistance drift, among which: (a) device engineering, through doping [65], multi-layered phase change materials [18], and (b) adaptive programming in regulating the amorphous volume through RESET [68], are most appreciated. The cell structure type is often used in classifying PCRAM devices. Over the years, several cell structures have been proposed [18]. Two types in particular are most widely recognised, which are: (a) mushroom cell/match stick [62] and, (b) line cell [69] (Figure 6(A,C)). A mushroom cell is constructed from vertically stacked layer. A special element of this design is the heater that makes direct contact with the phase change material. Heaters are typically made from resistive materials, such as tungsten and are required to be thermally stable and chemically inert. Significant Joule heating occurs at the contact area between the heater and phase change material film. Because the heating gets locally confined, heat losses are minimal, and energy consumption is greatly reduced. A line cell, on the other hand is a planar structure. It overcomes the deficiencies of the mushroom cells in ways that: (a) a very simple design, which is easy and quick

Figure 6. Illustration of popular PCRAM device geometries: (A) schematic of a mushroom cell, (B) actual mushroom cell; W plug is the heater (reprinted with the permission from Ahn et al. [71], © ACS Publications 2015), (C) schematic of a line cell, (D) actual line cell (reprinted with the permission from Lankhorst et al. [72], © Nature 2005), (E) schematic of an electrical probe storage system. Scanning probes are used to transform locally induce phase change, hence store data, and (F) atomic force micrograph of 20 nm sized crystallites (bright spots) written by a scanning probe (reprinted with the permission from Gidon et al. [69], © American Institute of Physics 2015).

for fabrication (b) underlying substrates, such as SiO2 being poor thermal conductors minimise heat losses, and (c) smaller volume of phase change material could be used, hence lower energy consumption. However, mushroom cells can be more densely integrated than line cells. This is the biggest drawback of line cells. The limit to scaling PCRAM is perhaps through the use of atomic force microscope probes. Tetra-bit per square inch data storage density has been demonstrated [69,70] using this approach. Figure 6(E,F) is the schematic of how an AFM probe could be used to locally induce phase change by Joule heating. Figure 6(F) shows such 20 nm sized crystallites in an amorphous matrix. Device challenges Present PCRAM device require improvements in their endurance limit. Failure in PCRAM devices is common, and is understood from their inability to exhibit reversible switching and/or show variation in conductance each time a switching cycle is performed. This is a serious challenge that PCRAM has to win if it is to truly compete against STT-MRAM and RRAM/CBRAM for the position of universal memory.

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significant attraction for its fast switching speeds and energy efficacy. This memory type uses a superlattice structure, made from stacked Sb2 Te and GeTe layers. Faster kinetics at low energy cost is resultant of solid–solid phase transformation rather than typical solid-liquid-solid between the crystalline and amorphous states. Ge atoms migrate (umbrella flip) to energetically favourable sites at the interface of the heterostructure stacking. Since migration is limited to only one direction (vertical), entropic losses are minimised, giving switching speed and reducing energy losses. Figure 8(A) illustrates a transmission electron micrograph of an interfacial memory cell. Figure 8(B) represents a schematic of the governing mechanism of interfacial memory. Recently, strains in the stacking have shown to improve device performance. This is understood from strain induced lowering of the activation barrier for Ge hopping to and fro the interface [56]. Super-fast phase transformation Figure 7. PCRAM device failure and performance graph: (A and B) scanning electron microscopy images of a mushroom cell before and after switching. After multiple cycle of switching, voids become apparent in the phase change material and are localised near the top electrode (while spots in B). A crack may form from coalescence of many voids, and due to mechanical stresses (reprinted with the permission from Hong et al. [73], © IOP science 2008), and (C) RESET current vs. contact area. Clearly, devices making lesser contact with the electrodes are more energy efficient (reprinted with the permission from Xiong et al. [20], © ACS Publications 2012).

Although, a number of failure mechanisms have been suggested, most appreciated arguments are: (a) void formation, and (b) segregation (Figure 7(A,B)). The former is a result of changes in the mass densities of the working material upon phase transformations, and is majorly observed at the electrode working bit interface, while the latter is attributed to electromigration of the constituent elements; which modifies the crystallisation kinetics, hence data retention and crystallisation rate properties. The key to improving endurance limit is using ultra-scaled phase change material volume. Ultra-scaled volume, nearing the size of single unit cell would experience crystallisation and amorphisation across the entire volume. This would eliminate elemental segregation and hence decrease failure probabilities. Smaller devices (decreased volume and interfacial contact area) also enhance the device performance (Figure 7(C)), as has been already described in detail.

Recent advances Interfacial memory Interfacial phase change memory [24], also called topological switching random access memory has attracted

Amorphous to crystalline phase transformation, which is SET, is always much slower than RESET, thereby limiting the writing speed of PCRAM devices. Fast crystallisation speeds can be achieved with phase change materials that have smaller crystallisation temperature. However, this would always occur at the cost of poor data retention or archival stability. Loke et al. [30] reported an unconventional solution to this, demonstrating crystallisation time as small as 500 ps. A local pre-ordering was induced in the phase change material using a small bias or incubation field, just before the threshold voltage pulse was applied. Pre-structural ordering in the amorphous matrix resultant of the thermal energy rendered by the incubation field decreased the time needed for the nuclei to precipitate, and consequently to grow. Figure 8(C) is schematic indicating the incubation field, and Figure 8(D) is a schematic illustrating corresponding pre-ordering in the amorphous matrix. Zero-mass density change phase change materials Crystalline-to-amorphous phase transformation is typically accompanied with a volume change. In Ge22 Sb22 Te55 , a decrease of volume by over 9% is reported [18,69]. Such cyclic variations in the volume during the switching cycles is one of the primary reasons for failure of PCRAM cells, and for years has remained a pestering challenge. Recently, GeTe-CuTe [74] and GaSb [75] based alloys were discovered, which show zeromass density change during phase transformations. A two-step crystallisation process explains the zero-mass density change behaviour. Essentially, crystallisation of more than one phase compensates for the volume change of the other phase through either expansion or shrinkage.



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Figure 8. Recent advances in PCRAM: (A) transmission electron microscopy image of an interfacial phase change memory (iPCRAM) cell (reprinted with the permission from Simpson et al. [24], © Nature 2011), (B) schematic of the mechanism governing switching in iPCRAM [81]. During RESET, Ge atoms change their coordination from four to sixfold by migrating away from the interface between GeTe and Sb2 Te3 layers; and vice versa for SET, (C) waveform of a small incubation voltage applied before the main pulse during SET, (D) pre-structural ordering effects on the crystallisation speed. The small voltage pulse represented in (C) provides sufficient thermal energy to locally order the amorphous phase. When the main pulse is applied, these pre-structured regions act as seed for nucleation and consequent crystallisation (reprinted with the permission from Loke et al. [30], © Science 2012), (E) A self-aligned GST nano-wire with carbon nanotube nano-gap electrodes of diameter 2.5 nm (reprinted with the permission from Xiong et al. [20], © ACS Publications 2012), and (F) graphene nano-gap-based PCRAM cell. GNR are the graphene nano ribbons which makes contact with GST (reprinted with the permission from Nam et al. [23], © American Institute of Physics 2015). Both (E) and (F) are examples of line cells.

CNT and graphene nano-gap electrodes

Graphene thermal layer

The concept of phase change memory with nanogaps-based devices has triggered a renewed interest in PCRAM. Carbon nano tubes (CNTs) nano-gap electrodes of sub-50 nm spacing have achieved singlehandedly [76] a great match of all the device requirements. These devices: Ge22 Sb22 Te55 thin film bridging CNT electrodes (diameter 2 nm), show switching energy as little as [20] ∼ 30 fJ. Figure 8(E) shows a selfaligned CNT nano-gap PCRAM device. Such reduced energy consumption is due to reduced interfacial contact area and reduced volume of the phase change material. Recently graphene nano-gaps [77] were demonstrated for PCRAM applications. Graphene electrodes can be more readily fabricated than CNT electrode. Figure 8(F) illustrates a graphene nano-gap PCRAM device.

Thermal layers can strongly influence the crystallisation kinetics, and regulate heat losses, thus, controlling the switching power. Graphene as a thermal layer was recently demonstrated [69,71] (Figure 6(B)). A 40% decrease in RESET current from graphene sandwiched between the heater and phase change material in a mushroom cell structure is reported [71]. The rationale behind this reduction follows the anisotropic properties of graphene, i.e. thermal conductivity a function of the crystallographic direction. Graphene’s poor out-of-plane thermal conductivity confines heat at the interface (graphene/ change material), thereby minimising thermal losses. Graphene as a thermal layer is also expected to improve endurance limits, as it can significantly reduce atomic migration/ segregation.

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Several other strategies to reduce programming power have been recently demonstrated. Nano-crystalline dispersions of a dielectric material [78] within the GST volume allow 68% reduction in RESET power. This follows from reduced thermal losses. Defects engineering of phase change films has also shown to reduce programming energy [79]. This follows from the rationale that pre-induced defects tend to enhance the carrier-lattice coupling, thereby resulting in improved Joule heating.


Projected memory devices Resistance drift as described earlier can be reduced through device engineering. Very recently, a new design concept called projected memory device was demonstrated [80] by IBM. This design decouples data storage, and data retrieval from each other through addition of a third non-insulating material, adjacent to the phase change material. This layer is referred as the projection. The electrical resistance of the projection layer must be an intermediate between the crystalline and amorphous state. Thus, at low fields, which the read operation is, current preferentially flows across the projection layer and not through more resistive amorphous volume. Because the phase change material is addressed only for write and erase operations, such isolation significantly minimizes resistance drift and noise. Photonic memory Nothing exceeds the speed of light. Using light for data communication as well for data storage and computation is heralded as the ultimate improvisation of computers. While a lot of success has been achieved with light based communication, little has been achieved with storage and computation. An all-photonic platform for storing, communicating and computing data was recently achieved with phase change materials [82]. This work deployed the contrast in the optical properties of phase change materials (between crystalline and

amorphous states), instead of the electrical resistance as we have seen thus far: crystalline state absorbs light more strongly than the amorphous phase does owing to its higher refractive index. Figure 9A illustrates the device concept [82,83] of storing binary numbers (1 and 0) with light. Data is stored as a function of light intensity in an on-chip waveguide: 1, when light through the waveguide is maximum, and 0 when a change (21% drop in intensity here) occurs (Figure 9B). The modulation of light intensity is achieved by placing a GST island over the waveguide. Light couples evanescently with GST, which absorbs more strongly when in the crystalline state than in the amorphous state. Consequently, this changes the transmission intensity. Beyond binary, multilevel data storage is also achievable by controlling the crystallite fraction in the phase change material [82].

The von-Neumann bottleneck Modern computers use the von-Neumann architecture, an approach where data is stored in a memory, and processed in the processor. The downside of this scheme is that electronic data has to shuttle between the memory and processor every time a mouse click is made. There is therefore no point using faster processors when the limiting factor is the transmission of data to and fro the memory across the bus bars. While concepts of multithreading, caching, RAMBUS, etc. have been well-appreciated, for future computing these approaches may not suffice. Light-based computing or photonics promises to be the key, but it could take years before an operational photonic chip is seen. Date storage and data computation within the same space on a chip, is idealised to be the solution for faster computing. The candidature of PCRAM in this context has been recently explored. Through demonstration of mathematical operations: addition, multiplication, division, factorisation [84], and Boolean algebra [85] at very fast speeds and decreased energy consumptions, their applicability has been conclusively proven.

Figure 9. Light for data storage: (A) illustration of a photonic memory device. Phase change material (GST) placed on top of a waveguide modulates the light intensity, and (B) binary level data storage as a function of the transmission percentage in the waveguide. (Reprinted with the permission from Ríos et al. [82], © Nature 2015).


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Figure 10. (A) Top is a mushroom cell, bottom is the resistance of the device as function of pulse number and right is a simulation revealing how the crystalline phase evolve (reprinted with the permission from Wright et al. [86] , © Wiley 2012) , and (B) factorisation of the number 6 using a PCRAM device. The cell is designed to switch from amorphous to crystalline after two excitation pulses. The resistance of the cell falls after three paired pulses, indicating 2 is factor of 6.

Essentially, DC pulses of different amplitudes, when swept across the working volume in varied time gaps, can sequentially change its state. Each time a pulse is swept in the amorphous matrix, the number and sizes of the crystallites grow. This means the resistance of the PCRAM material decreases as a function of number of and amplitude of pulses. It is this property of accumulation change, which enables data storage and computation in phase change materials. Figure 10(A) illustrates this concept. Figure 10(B) shows factorisation of 6 using the accumulation property of phase change materials. A point to note is that resistance drift is of lesser concern in these devices since the material is taken above T g each time a excitation pulse is applied. A scheme such as this not only makes computation faster, but through offering multilevel data storage, also enhances the storage capacity.

Neuromorphic computing For neuromorphic computing, the key lies in the emulation of neurons [87]. Devices emulating neurons should possess some kind of memory, which could be either in the form of synaptic weight (synapse is a tissue that connects dendrites (think of it as inputs) to axons (think of it as output), and synaptic weight defines the strength of the connection) or as the membrane potential of the neuron. Majority of the research relating PCRAM has concerned using two states (amorphous and crystalline) to store data. What however remains unappreciated is the potential for multilevel programming with these materials, through the so-called accumulative property discussed in the previous section. As described earlier, phase change materials can be switched across different intermediate resistance states two and fro with electrical pulses [88]. This behavior has enabled designing of electronic architectures which mimic the functionalities of synapses [89,90]. While other mem-resistors or resistive memories

namely conductive-bridge (CBRAM)/programmablemetallisation cell (PMC), and oxide-resistive (OXRAM) memory can in principle be used, PCRAM devices have received special attention for reasons such as (a) CMOS compatibility, (b) greater scalability, (b) reduced programming power and variability across devices, (c) superior endurance limit, and (d) technological maturity. Processes unique to a brain, such as image recognition [86], and spike-timing-dependent plasticity [87] have been successfully demonstrated using PCRAM devices. These demonstrations have envisaged low-power neuromorphic computing platforms, capable of processing information through learning and adaptation. Figure 11 illustrates a schematic of an artificial neuron, where a PCRAM device emulates the synapses. Recently, scientists at IBM have demonstrated a prototype stochastic phase change neuron [91], which stores membrane potential in the phase configuration of a phase change material, thereby imitating the neuron soma. This piece of work is worthy of praise as it brings to advantage and use the unavoidable intrinsic variations in PCRAM cell’s resistance typically observed after every run and over time (remember from previous discussions that stochastic resistance variations has been a severe downside for the PCRAM data storage technology).

Concluding remarks Although phase change materials based phase change memory remains commercialised in the optical data storage market for more than two decades, its applicability remains lesser tapped in electronic data storage. With recent announcements on the end of Moore’s law, phase change memory (PCRAM) is renewing interest as a promising next-generation technology for electronic data storage and computation. This article has taken a holistic approach to brief the evolution of PCRAM, and collate this technology against other existing/emerging technologies; reasoning why

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Acknowledgements The author acknowledges a Felix Scholarship that funds his DPhil and Armourers & Brasiers for their support. The author is grateful to his supervisors (Prof Harish Bhaskaran and Dr Jan Mol) for their constant guidance and to his colleagues (IIija Rosovic and Benjamin Porter) for proofreading the manuscript. The author also acknowledges the constant support from Dr Sabiha Nazili Naqvi, which made this review possible.

Disclosure statement No potential conflict of interest was reported by the author.


References Figure 11. Artificial neuron based on a PCRAM cell: the artificial neuron consists of Input (dendrite), PCRAM cell (neural soma) which is the active component that stores information (synaptic weight) as its phase configuration and the Output (axon). The strength of the neuron, which is its ability to communicate with other neurons, depends on the conductance of the PCRAM cell.

this technology has an upper-edge. A detailed record of phase change material characteristics, namely threshold switching, atomic bonding, and phase transformation is presented. Materials classification based on the crystallisation mechanism (nucleation dominated and growth dominated) and cell’s characterisation (cooling rate, crystallisation kinetics, archival stability and endurance) is elaborated. Influence of device scaling on the material properties is critically reviewed. Various device design variables, such as cell type (mushroom cell and line cell), compositional variants, cladding layers, resistance drift, and contact electrodes are described using a material science perspective. While discussing the challenges associated with PCRAM technology, recent advances: interfacial memory, super-fast crystallisation, zero-mass density-assisted phase transformation, graphene and CNT-based devices, projected memory cell and photonic memory are discussed. Furthermore, the prospectus of PCRAM in combating the von-Neumann architecture is elaborated. PCRAM devices as emulations for synapses in neuromorphic computing are also detailed. It is truly remarkable as to how phase change materials can be exploited for data storage applications, in the form of PCRAM, and further be extended for computation applications, which span from simple logic operations to sophisticated neuromorphic computing. It is only recently that major advances on this technology were made, which have overcome the once thought limitations of PCRAM [92,93]. This has resulted to ground breaking research, and commercialisation, such as of 3D XPoint data storage technology [94]. For these reasons, it is the author’s strong belief that PCRAM will get a wider recognition and appreciation in the years to come.

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