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Abstract—We present a study about loss analysis in both-sides- contacted silicon solar cells from a porous silicon (PSI) layer transfer process. Experimental ...
IEEE TRANSACTIONS ON ELECTRON DEVICES, VOL. 59, NO. 4, APRIL 2012

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19% Efficient Thin-Film Crystalline Silicon Solar Cells From Layer Transfer Using Porous Silicon: A Loss Analysis by Means of Three-Dimensional Simulations Jan Hendrik Petermann, Tobias Ohrdes, Pietro P. Altermatt, Stefan Eidelloth, and Rolf Brendel

Abstract—We present a study about loss analysis in both-sidescontacted silicon solar cells from a porous silicon (PSI) layer transfer process. Experimental results achieved by a variation of the rear-side contact geometry are characterized by different techniques such as electroluminescence and quantum efficiency measurements and reproduced by 3-D simulations using Sentaurus Device. Since such a device simulation does not include resistive losses in the metallization, we use a network simulation to account for losses caused by the grid. Considering the optimal contact geometry, the simulations indicate the power losses in the emitter, at the rear-side contacts, in the base, and in the metallization grid to be in the same order of magnitude. Index Terms—Kerfless, layer transfer, loss analysis, porous silicon (PSI).

I. I NTRODUCTION

L

AYER transfer processes for silicon solar cells are promising candidates to reduce cell costs because kerf losses are greatly reduced since one substrate wafer can be reused many times. An already well-developed process is the porous silicon (PSI) process introduced by Tayanaka and Matsushita [1] and Brendel [2]. Recently, we have demonstrated the highefficiency potential of this material by reporting a new independently confirmed record efficiency value of 19.1% for this type of solar cell [3]. The cell presented in [3] is the best one of a batch where we varied the back contact geometry. In particular, we varied the distance (pitch) between the back contact pads produced by laser contact opening (LCO) but kept the fraction of metallization constant at (5 ± 0.5)%. The results of the whole batch are presented and characterized Manuscript received October 27, 2011; revised December 7, 2011; accepted December 22, 2011. Date of publication February 6, 2012; date of current version March 23, 2012. The review of this paper was arranged by Editor A. G. Aberle. J. H. Petermann, T. Ohrdes, and S. Eidelloth are with the Institute for Solar Energy Research Hamelin, 31860 Emmerthal, Germany (e-mail: [email protected]; [email protected]; [email protected]). P. P. Altermatt is with the Department of Solar Energy, Institute of SolidState Physics, Leibniz University of Hanover, 30167 Hannover, Germany (e-mail: [email protected]). R. Brendel is with the Institute for Solar Energy Research Hamelin, 31860 Emmerthal, Germany, and also with the Department of Solar Energy, Institute of Solid-State Physics, Leibniz University of Hanover, 30167 Hannover, Germany (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TED.2012.2183001

Fig. 1. Layout of the layer-transferred PERC as processed in this paper. The pitch is varied between 250 and 1500 μm.

here. For loss analysis, we use the process simulation software Sentaurus Device [4] in combination with a network simulation of the whole cell grid applying a recently in-house developed graphical user interface for LTSpice [5], [6]. II. S OLAR C ELL P REPARATION The detailed process flow is described in [3]; thus, we only give a short summary here. The structure of the fabricated 43-μm-thin solar cells shown in Fig. 1 is a passivated emitter and rear cell (PERC) design [7]. The epitaxially grown base material from the PSI process is boron doped with a resistivity of 0.5 Ω · cm. After lifting off the epitaxial layer, we process our cells freestanding. The textured front side receives phosphorus diffusion from a POCl3 source in a quartz furnace, resulting in a sheet resistance of 100 Ω. On top of the emitter, we deposit an AlOx tunnel layer [8], which serves as contact passivation and diffusion barrier against the evaporated aluminum grid. As antireflection coating, we use a SiNx double stack consisting of 10 nm SiNx with a refractive index of 2.4 (at a 633-nm wavelength) and nominal 100 nm SiNx with a refraction index of 2.05. On the back side of the cell, we deposit an Al2 O3 /SiNx double layer to receive a good passivation quality and a good back-side mirror to enhance light trapping in the longer wavelength range. We split the batch into five groups of cells with different pitches of 250, 500, 750, 1000, and 1500 μm, which are defined by LCO. Each group contains at least five and up to eight cells. The areal fraction of the opened dielectric layer is

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TABLE I M ODELS AND I NDEPENDENTLY M EASURED I NPUT PARAMETERS U SED FOR THE D EVICE S IMULATIONS

constant at about (5 ± 0.5)%, resulting in different sizes of the single rectangular pads. Evaporated aluminum on the entire rear side permits contacting the base through the LCOs and serves as a back-side mirror.

parameters by fabricating different test samples, which we describe in the following. The extracted set of parameters is shown in Table I. A. Contact Resistances

III. D ETERMINATION OF S IMULATION I NPUT PARAMETERS A 3-D numerical device simulation is a powerful tool for understanding a solar cell. Nevertheless, every simulation is just as strong as its input parameters are. In order to simulate and extract realistic values for the different losses, it is essential to determine all physical values that are input parameters to the simulation, such as the contact resistances, the injectiondependent bulk lifetime, the surface recombination velocities, and the diffusion and generation profiles. We extract all these

In order to quantify the specific contact resistance between the front metal contacts and the Si substrate, we prepare 1.5-Ω · cm float-zone (FZ) p-type wafers that are (2.5 × 2.5) cm2 large and 300 μm thick in the following way. The samples receive a random pyramid texture in a KOH/isopropanol solution and subsequently the same phosphorus diffusion as the cells (100 Ω). We remove the residual phosphor silicate glass in a 1% HF solution and deposit two cycles of AlOx by plasma-assisted atomic layer deposition (ALD) onto the

PETERMANN et al.: SILICON SOLAR CELLS FROM LAYER TRANSFER USING PSI

Fig. 2. Lifetime mapping of a test sample by dynamic-ILM. The square indicated by dashed lines is the area that is evaluated and therefore avoids edge effects.

surface. Finally, we evaporate 3 μm of aluminum through a shadow mask that has a pattern suitable for a later measurement of contact resistivity Rc,emit with the transfer-length method (TLM) [9]. For the determination of Rc,base of the evaporated rear aluminum contacts, we also take a (2.5 × 2.5) cm2 large and 300-μm-thick FZ p-type sample but with a resistivity of 0.5 Ω · cm as the base material. We deposit the same Al2 O3 /SiNx stack as used for the rear-side passivation of the cell and totally ablate it with our LCO technique. Finally, we evaporate 3 μm of aluminum through the same type of shadow mask as mentioned above. The TLM measurements result in a specific contact resistance of ρc,emit = (0.94 ± 0.45) mΩ · cm2 and ρc,base = (3.40 ± 0.29) mΩ · cm2 . B. Injection-Dependent Bulk Lifetime As test samples, we use freestanding wafers coming from the same epitaxial run as the cell material. We remove the residual PSI layer in a KOH solution and clean the sample surfaces by RCA. After this, we passivate both sample sides with 30 nm of Al2 O3 deposited by plasma-assisted ALD at 200 ◦ C. The passivation quality is most effective when tempered in a quartz furnace at 425 ◦ C for 20 min under nitrogen atmosphere. We measure the injection-dependent effective excess carrier lifetime τeff of the solar cell material by the quasi-steadystate photoconductance decay method (QSSPC) [10] using a Sinton Wafer Lifetime Tool in generalized mode [11] for excess carrier densities Δn ranging from 1 × 1013 to 1 × 1017 cm−3 . The quasi-steady-state mode is necessary since τeff is clearly below 100 μs and cannot be measured in transient mode. It has the disadvantage that we need a calibration factor for the optical generation, which is, in fact, not exactly known. Additionally, τeff may be underestimated by recombination at the edges of the test sample [12]. In order to take advantage of the QSSPC measurement that allows a measurement in a wide range of Δn and to avoid the disadvantages of calibration, we additionally measure the samples with the calibration-free dynamic infrared lifetime mapping (dynamic-ILM) technique (see Fig. 2) [13].

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Fig. 3. Effective lifetime of the test sample measured with different techniques: quasi-steady-state photoconductance (black circles) shifted to dynamic-ILM values (red stars) for calibration, photoconductance-calibrated photoluminescence imaging (green squares), and simulated lifetime (blue triangles).

We evaluate τeff by taking the arithmetic average of the area shown in Fig. 2 to avoid edge effects. Afterward, we shift in Fig. 3 the QSSPC curve to the data obtained with the dynamicILM measurement and use the latter as a calibration. For τeff at low injection below Δn = 1 × 1013 cm−3 , which is necessary for simulations under short-circuit current conditions, we use the photoconductance-calibrated photoluminescence imaging method (PCPLI) [14], shown as squares in Fig. 3. We fit the data in Fig. 3 to the Shockley–Read–Hall (SRH) theory using Sentaurus. As the data cannot be fitted with a single defect, we assume two midgap recombination centers with different cross sections for electrons and holes, respectively. In addition, we assume identical surface recombination velocities of 4.8 cm/s for both sides gained from references (see the next section) and fit the experimental curve with the minority and majority carrier lifetimes (τn0 , τp0 ) as free parameters. With this simulation, we extract τn0 = 13 μs and τp0 = 28 ms for the first recombination center and τn0 = 40 μs and τp0 = 1.6 ms for the second one. The SRH theory describes the measurements reasonably well (see Fig. 3) and is used in the simulations. The simulation uses the silicon models listed in Table I. C. Rear-Side Surface Recombination Velocity For the extraction of the effective surface recombination velocity Seff at the rear side of the cell, we use a (2.5 × 2.5) cm2 large and 300-μm-thick FZ p-type wafer with a resistivity of 0.5 Ω · cm as a test sample. After passivating both sides of the sample with the same Al2 O3 layer as used for the rear side of the cell and the lifetime sample, we measure τeff by the transient photoconductance decay method using a Sinton Wafer Lifetime Tool. We extract Seff from these measurements with the relation 1/τeff = 1/τbulk + 2Seff /W , where τbulk is the lifetime in the volume of the sample and W is the sample thickness. We assume τbulk to be limited by Auger recombination, which is given by the parameterization of Kerr and Cuevas [15], and calculate the maximum Seff value as (4.8 ± 2) cm/s, which is rather independent of Δn.

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Fig. 4. (a) Doping profile of the emitter measured by the ECV technique. (b) Effective lifetime measurement of the J0e sample showing the transition between transient and steady-state measurements when the Flash is turned off.

D. Doping Profile In order to measure the electrically active doping profile, we take a polished (100)-orientated p-type reference wafer with a resistivity of 1.5 Ω · cm and use the identical phosphorus diffusion process as for the solar cells. The profile is measured by the electrochemical capacitance–voltage method (ECV) using a CVP21 profiler (Ingenieurbüro WEP, Furtwangen, Germany) [see Fig. 4(a)]. The dopant density at the surface is 4 × 1020 cm−3 .

E. Surface Recombination Velocity of the Emitter Surface We extract the surface recombination velocity of the emitter surface Sfront from τeff measurements in the following way. We diffuse the emitter profile onto both textured surfaces of a (2.5 × 2.5) cm2 large and 290-μm-thick FZ p-type wafer having a resistivity of 200 Ω · cm and deposit the previously described AlOx tunneling layer and the antireflection coating. Finally, we anneal the sample as the solar cells, i.e., on a hot plate at 350 ◦ C, to maximize the passivating action. We then measure τeff again with the Sinton Wafer Lifetime Tool in generalized mode using a longtime Flash. This time, we can check our calibration factor by considering the transition when the Flash turns off and the quasi-steady-state measurement turns into a transient one, where no calibration is necessary [see Fig. 4(b)]. If the J0e evaluation done by Kane and Swanson [16] is continuous after a settling time, the calibration factor is valid. Doing this, we measure a J0e of (270 ± 10) fA/cm2 . To determine Sfront , we simulate J0e with the input parameters listed in Table I using the doping profile described above at both sides of the simulated test sample. The used software is again Sentaurus. As Sfront is the only free parameter, we ramp it until the simulated J0e matches the measured one. This way, we obtain Sfront = 7.7 × 106 cm/s with 6 × 106 and 1 × 107 cm/s as lower and upper limits, respectively. This value is about ten times higher than that observed in planar emitters [17]. This is because our samples are not fired and our emitter is diffused into a textured surface while we simulate a planar surface; thus, we underestimate the volume and the surface of the emitter in our simulation. The lower recom-

bination rate is therefore compensated by the higher Sfront value. F. Generation Profile Apart from the input parameters above, the simulation also requires an optical generation profile G(y), where y is the depth coordinate indicated in Fig. 1. We calculate G(y) with the ray tracer Sunrays [28] using a simulation domain that includes one pyramid and its underlying 43-μm-thick base material. With this approach, we cannot account for the rear metal contact pattern. Therefore, we simulate with the rear side fully passivated to obtain Gpass and with the rear side fully metallized to obtain Gmet . We then take the average of the two profiles weighted by the metallization fraction and map it laterally onto the domain of the device simulation. As the device domain is planar, we scale the pyramid size as explained in [29]; otherwise, G would be maximal at the base of the pyramid, i.e., a few micrometers below the emitter. IV. D EVICE S IMULATION A. 3-D Device Simulation For the device simulation, we use the models and our experimental data listed in Table I. When doing the variation of the pitch, we just change the geometry of the unit cell according to the pitch but leave all input parameters constant. We vary the pitch from 250 to 2000 μm. In order to keep the computing time as short as possible, we chose the smallest possible simulation domain, which has to be consistent with the periodicity of the front fingers and the pitch of the LCOs. With these simulations, it is possible to calculate the recombination losses in the various device regions and to determine the series resistance to arrive at a detailed loss analysis. B. Network Simulation of Complete Solar Cells Since the device simulation does not account for resistive losses in the front metallization grid and the homogeneous rear metallization, we additionally perform circuit simulations using an in-house developed graphical user interface for LTSpice [6],

PETERMANN et al.: SILICON SOLAR CELLS FROM LAYER TRANSFER USING PSI

Fig. 5. Schematic of the first level of the network simulation. The nodes are numbered. Each node is connected to a J−V “device.”

which is an open-source simulation tool commonly used for electronic network simulations. Our circuit is grouped into two levels. The top level represents the front metallization fingers, the space between them, the edge areas where no fingers are present, and the busbar, as shown in Fig. 5. The J−V curves from the device simulation serve as instances at the nodes. We discretize the cell into 20 × 29 nodes; thus, there are 20 nodes in the y-direction as this is the number of metal fingers and 29 nodes in the x-direction to guarantee a sufficient resolution of the potential drop along each metal finger and at the edge area. The second level of the circuit represents the fully metallized back side of the solar cell, where the resistive losses are rather negligible. As an input parameter, we use the specific bulk resistivity of aluminum, which is 2.65 × 10−6 Ω · cm in combination with the grid geometry. The resistances of the edges and the finger spaces are calculated by using the sheet resistance of our emitter. However, the connections representing the finger spaces have no influence on the results; therefore, they could be omitted. The contact points are chosen as in the experiment, which means that the back side is connected everywhere and that the front side is connected at only one node (20, 15). As we use the same simulated J−V curve for each node, we assume the solar cell to be completely homogenous even at the edges. V. R ESULTS AND D ISCUSSION A. Solar Cell Parameters From Experiment/Simulation Fig. 6 shows a comparison between the in-house measured cell data and our simulations as a function of pitch. From the experimental data, we see an increasing short-circuit current and an increasing open-circuit voltage with growing pitch. The simulation reproduces this behavior very well. Again, note that all parameters are held constant, and only the simulation geometry is adapted to the corresponding experimental cell geometry. The simulated results indicate a saturation of Jsc and a decreasing Voc when going to pitches larger than 1500 μm. Since our simulation is at 300 K instead of 298.15 K, where the cells are measured, we apply a temperature correction according to the following equation [30]: dVoc Eg0 /q − Voc + 3kT /q = dT T

(1)

where Eg0 is the linearly extrapolated zero-temperature band gap, k is the Boltzmann’s constant, and T is the temperature

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where the open-circuit voltage Voc is measured. Nevertheless, it seems that we slightly underestimate the contact recombination since the experimental decrease in Jsc and Voc at smaller pitches is stronger than that in the simulation. The experimental fill factor F F indicates only a slight decrease toward larger pitches. The F F at a pitch of 250 μm seems to be out of this trend. For this, we examine the electroluminescence (EL) signal of all these cells (not shown here) and see that every single cell with a pitch of 250 μm has at least one interrupted finger or other high recombinative regions, which explains this behavior. When omitting this pitch, the simulated trend to a lower F F with increasing pitch is also observable in the experiment. This can be understood when considering the internal series resistance Rs,intern of the cells, which includes resistive losses in the base and the emitter and the contact resistances. We calculate this value with Sentaurus by simulating one voltage near the maximum power point (MPP) at two different illumination intensities and applying the double-light method [31] to calculate Rs,intern . The data shown in Fig. 8 together with F F reveal that the decrease in F F is caused by an increasing Rs,intern , resulting from increasing lateral carrier transport to the LCOs. Note that the decline in F F with a higher pitch due to an increasing Rs,intern is partly compensated by an increasing Voc . The deviation from the simulation and the experiment in F F of about one percentage point absolute can have several reasons. When comparing the experimentally determined series resistance, e.g., for the cells with a pitch of 1500 μm, we determine an average value of 0.90 Ω · cm2 by the double-light method. However, when extracting the series resistance by the same method for this cell type from the simulated data, we extract a series resistance of only 0.63 Ω · cm2 . Thus, it seems that we underestimate the contact resistances. The difference between these series resistances would cause a difference in F F of 1.4% points absolute when applied to the two-diode model. Second, the measurement error in F F is typically at least 0.5% absolute (e.g., at ISE CalLab, Germany). Third, cell inhomogeneities and the fact that J−V characteristics at the edges will change when there is no metallization can give rise to this observed deviation. The edges are here simplified by assuming the emitter sheet resistance at this edge area but with the same J−V characteristics as under the fingers. The resistive losses simulated by our network simulation result in a drop in F F of about 1.8% absolute in comparison with the Sentaurus simulation alone. As a confirmation of this network simulation, we compare it with experimental EL images. From the EL signal difference, for example, along the busbar, it is possible to calculate the difference in voltage between two areas when assuming homogenous optical properties [32]. The EL signal Φ then reads V

Φ ∼ CNA n ∼ e Vt

(2)

where C is a setup-dependent proportionality factor assumed to be constant all over the cell, NA is the base doping concentration, and n is the electron concentration. Since we assume the first two factors to be constant, the signal is proportional to n and therefore proportional to the third term given in (2), where

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Fig. 6. Comparison between the experiment (red stars), the 3-D Sentaurus simulation (black circles), and with the LTSpice network simulation (blue triangles). The error bars of the experimental data represent the highest and lowest measured values, and the star represents the median of all the cells of the same pitch. All cells are measured in-house under standard test conditions. The fill factor graph in (c) also contains the simulated internal series resistance (green squares). The curves are guides to the eye.

Fig. 7. Comparison between the EL image and our network simulation of the entire (2.5 × 2.5) cm2 cell using a combination of Sentaurus, LTSpice, and our in-house developed simulation environment. The simulation includes the edges of the solar cell where no fingers are present.

V is the local voltage and Vt is the thermal voltage. We observe the EL signals at the beginning and at the end of the busbar, i.e., Φ1 and Φ2 , respectively. When taking the ratio between these two signals, we can solve the equation and find the difference in voltage ΔV of the different areas, i.e.,  ΔV = ln

Φ1 Φ2

 Vt .

(3)

We evaluate all 27 EL images of our cells this way and build the arithmetic average of ΔV . This leads to a value of (14 ± 3) mV. Our network simulation gives a voltage drop of 17 mV, which fits within the error bars of the experiment. Fig. 7 shows a qualitative comparison between the EL image and the network simulation. Altogether, we see a good agreement between the experiment and the simulation. This, in addition to the fact that all

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Fig. 8. Simulated recombination current losses (bars) (a) under short-circuit conditions and (b) at the MPP. The arithmetic average of the inverse effective diffusion length extracted from quantum efficiency measurements is shown by the circles in (a). (Blue bars) Recombination losses due to the contacts. (Red bars) Losses in the base. (Black bars) Losses in the emitter.

simulation parameters apart from the contact recombination velocity are extracted from experimental test structures, justifies our next step, which is the loss analysis. B. Current Loss Analysis As the performance behavior of the whole series of fabricated cells is reproduced in the preceding section and is based on independently measured input parameters, we can now reliably calculate the internal losses of these cells. We analyze the recombination losses by dividing the cell into three subregions— emitter, base, and contacts—and by integrating over the SRH and Auger recombination rates, as well as the associated surface recombination rates. This holds for the external current J(V ) = qG − qR(V ), where G is the total photogenerated current, R is the total recombination rate, and q is the unit charge. This relationship allows us to compute the recombination at the contacts by qRcont = qG − J − qRem − qRbase . Obviously, this method allows no separation between the front- and backside contacts. Therefore, we varied the surface recombination velocity at the front contacts and noticed no significant influence on cell parameters. Hence, the total contact recombination is dominated by the recombination at the rear-side contacts. Fig. 8 shows the amount of recombination separated into the three device regions, expressed as current densities (i.e., qR), under short-circuit [see Fig. 8(a)] and at the MPP condition [see Fig. 8(b)]. At V = 0, we see with a growing pitch a decreasing influence of the rear-side contacts, whereas the contributions of the emitter and the base remain almost constant. The behavior of the recombination at the contacts, particularly the stronger drop between pitches of 250 and 500 μm, can be understood when considering the diffusion length. At the Jsc condition, there is Δn ≈ 5 × 1012 cm−3 , leading to τb ≈ 12 μs, which, in turn, corresponds to a diffusion length about 167 μm. In a first approximation, every contact collects minority carriers within one diffusion length. At a pitch of 250 μm, the collection regions between two contact points

overlap and therefore collect carriers from the whole rear side, which is not the case at pitches exceeding 500 μm. This explains the larger difference in current loss that we also see in the experiment. To compare the simulation results with experimental data, Fig. 8(a) also shows inverse effective diffusion length L−1 eff as a function of pitch. It shows a similar behavior as the current losses do. Both have the prominent strong drop between pitches of 250 and 500 μm and saturate for larger pitches. Since these values are both functions of Seff of the rear side, this behavior is consistent. At the Vmpp condition, the total losses decrease with increasing pitch, as expected from an increasing energy conversion efficiency value. This decrease in recombination losses is primarily caused by a decrease in contact recombination, which compensates the increase in recombination in the emitter and the base. The latter occurs because a reduction in contact recombination increases the average difference in the quasi-Fermi potentials in the base and, due to minority carrier injection across the p-n junction, in the emitter. An important effect arises when comparing both operating conditions. While for a pitch of 1500 μm under the Jsc condition the contact recombination is the major loss channel, it changes under Vmpp conditions, where the losses in the emitter become dominant. This may lead to a misinterpretation of the main loss channel, for example, when only using a quantum efficiency measurement to extract the losses. Such measurements are performed under short-circuit conditions, and therefore, one would conclude that the contacts are the main loss channel instead of the emitter near the MPP. After all, for this cell process, a pitch between 1000 and 1500 μm seems to be the optimal choice. This is in agreement with our experimental result since our record efficiency cell presented in [3] has a pitch of 1500 μm. In order to compare the internal losses of this cell with those due to the cell grid, we approximate the power loss of each loss channel as the product of Jloss and Vmpp . The power loss in the grid is taken as the difference in output power with and without our network

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simulation. This leads to the following distribution of power losses at Vmpp : 0.59 mW/cm2 in the emitter, 0.52 mW/cm2 at the rear-side contacts, 0.4 mW/cm2 in the base, and 0.44 mW/cm2 due to the metallization grid.

VI. C ONCLUSION We performed a loss analysis for thin-film silicon solar PERC cells from layer transfer with varying pitches by 3-D simulations and also include resistive losses due to the metallization grid. The input parameters for the simulation came from experimental test samples, and in general, results are in good agreement with the experimental values, except for the F F . The deviation in this value can be explained by an underestimation of the contact resistances and cell inhomogeneities. For this cell process, a pitch between 1000 and 1500 μm is the best choice. In this region of the pitch, the back-side contacts are the main recombination loss channel under Jsc conditions, whereas this behavior changes at the MPP, where the losses in the emitter slightly exceed the losses of the contacts. Nevertheless, at a pitch of 1500 μm, all power loss channels, including the power dissipation in the metallization grid, are almost equally distributed.

ACKNOWLEDGMENT The authors would like to thank R. Krain and S. Herlufsen for the help with PCPLI measurements and evaluation. R EFERENCES [1] H. Tayanaka and T. Matsushita, “Separation of thin epitaxial Si films on porous Si for solar cells,” (in Japanese), in Proc. 6th Sony Res. Forum, 1996, p. 556. [2] R. Brendel, “A novel process for ultrathin monocrystalline silicon solar cells on glass,” in Proc. 14th Eur. Photovolt. Solar Energy Conf., 1997, pp. 1354–1357. [3] J. H. Petermann, D. Zielke, J. Schmidt, F. Haase, E. Garralaga Rojas, and R. Brendel, “19%-efficient and 43 μm-thick crystalline Si solar cell from layer transfer using porous silicon,” Progr. Photovol., Res. App., vol. 20, no. 1, pp. 1–5, Jan. 2012, DOI: 10.1002/pip. [4] Sentaurus Process User Guide, Version E-2010.12, Synopsys, Inc., Mountain View, CA, 2010. [5] S. Eidelloth, F. Haase, and R. Brendel, “Simulation tool for equivalent circuit modeling of photovoltaic devices,” J. Photovol., submitted for publication. [6] Oct. 26, 2011. [Online]. Available: http://www.linear.com/designtools/ software/ [7] A. Blakers, A. Wang, A. Milne, J. Zhao, and M. Green, “22.8% efficient silicon solar cell,” Appl. Phys. Lett., vol. 55, no. 13, pp. 1363–1365, Sep. 1989, DOI: 10.1063/1.101596. [8] D. Zielke, J. H. Petermann, F. Werner, B. Veith, R. Brendel, and J. Schmidt, “Contact passivation in silicon solar cells using atomic-layerdeposited aluminum oxide layers,” Phys. Stat. Sol. RRL, vol. 5, no. 8, pp. 298–300, Aug. 2011, DOI: 10.1002/pssr.201105285. [9] W. Shockley, “Research and investigation of inverse epitaxial UHF power transistors,” Air Force Atomic Lab., Wright-Patterson Air Force Base, Dayton, OH, Report Al-TOR-64-207, 1964. [10] R. A. Sinton and A. Cuevas, “Contactless determination of current– voltage characteristics and minority-carrier lifetimes in semiconductors by quasi-steady-state photoconductance data,” Appl. Phys. Lett., vol. 69, no. 17, pp. 2510–2512, Oct. 1996, DOI: 10.1063/1.117723. [11] H. Nagel, C. Berge, and A. G. Aberle, “Generalized analysis of quasi-steady-state and quasi-transient measurements on carrier lifetimes in semiconductors,” J. Appl. Phys., vol. 86, no. 11, pp. 6218–6221, Dec. 1999, DOI: 10.1063/1.371633.

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PETERMANN et al.: SILICON SOLAR CELLS FROM LAYER TRANSFER USING PSI

Jan Hendrik Petermann received the Diploma degree in technical physics from Leibniz University Hanover, Hanover, Germany, in 2010. He is currently working toward the Ph.D. degree at the Institute of Solar Energy Research Hamelin, Emmerthal, Germany. In 2008, he worked for an internship at the Forschungszentrum Jülich, Jülich, Germany, in the field of residual gas analysis of a PECVD system for thin-film silicon solar cell fabrication. From 2009 to 2010, he worked on his diploma thesis with the Institute of Solar Energy Research Hamelin on the contact formation of aluminum-doped zinc oxide to phosphorus-doped silicon. He is currently with the Si Thin-Film Group of Dr. S. Kajari-Schröde, working on the development of layer-transferred silicon solar cells and their analysis.

Tobias Ohrdes received the Diploma degree in physics from Leibniz University Hannover, Hanover, Germany, in 2011. He is currently working toward the Ph.D. degree at the Institute for Solar Energy Research Hamelin, Emmerthal, Germany. His Diploma thesis was focused on high-efficiency solar cells based on numerical device modeling. He is currently with the High-Efficiency Silicon Wafer Solar Cells Group of Prof. Dr. N.-P. Harder, working on the modeling of semiconductor devices.

Pietro P. Altermatt received the Ph.D. degree from the University of Konstanz, Konstanz, Germany, in 1996, for his activities at the Centre for Photovoltaic Applications, University of New South Wales, Sydney, Australia. He is currently with Leibniz University of Hanover, Hanover, Germany, as Head of the Simulation Group, where Si solar cells are investigated by means of numerical device simulations and by optical modeling. His research interests include the design of test samples for the extraction of device or silicon material parameters, the improvement of simulation models, and the development of solar cell design strategies that are tailored to meet specific demands in terms of materials, geometries, fabrication processes, and applications.

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Stefan Eidelloth received the Diploma degree in physical engineering from the Münster University of Applied Sciences, Münster, Germany, in 2006, on the characterization of laser-induced damages that appear during the structuring of silicon wafers for high-efficiency solar cells. He is currently working toward the Ph.D. degree at the Institute for Solar Energy Research Hameln, Emmerthal, Germany. In 2005, he was with the R&D Department, Cohrent Inc., Santa Clara, CA, working on diodepumped solid-state laser. In his work, he develops simulation software and metallization processes for crystalline silicon solar cells.

Rolf Brendel received the Diploma degree in physics in nuclear fusion research and the First German State Board Examination in Mathematics (teacher degree level) from the University of Heidelberg, Heidelberg, Germany, in 1987 and 1988, respectively, and the Ph.D. degree for his research on infrared spectroscopy from the University of Erlangen-Nuremberg, Erlangen, Germany, in 1992. He then joined the Max Planck Institute for Solid State Research, Stuttgart, Germany, to study the physics of thin-film silicon solar cells and the thermodynamics of photovoltaic power conversion. In 1997, he became the Head of the Division for Thermosensorics and Photovoltaics, Bavarian Center for Applied Energy Research, where he was involved in research on imaging diagnostics of solar cells. In 2004, he joined the Institute of Solid State Physics, Leibniz University of Hanover, Hanover, Germany, as a Professor. He is also the Director of the Institute for Solar Energy Research Hamelin, Emmerthal, Germany.