Improving of a Perovskite Solar Cell Structure by Optimizing Several Simulation Parametric Factors in SCAPS-1D Program

doi:10.21203/rs.3.rs-3457461/v1

Utilizing ABX₃-based perovskites for solar cell applications proved to be a sustainable choice for solar cell applications. Using the data screening, the present study has concentrated on selecting the suitable PSC structure among some considered common organic-inorganic solar cell structures through the SCAPS-1D simulation tool. After good data selection, the optimization process was begun. However, it occurred in several phases including PSCs’ layer property, working point, and configuration optimization. The results of active layers’ property optimization represented that, choosing a cell with relatively high absorber thicknesses, low transporting materials thicknesses, and low active layers doping densities is better. Regarding working point optimization, the simulation results suggested lower temperatures, series resistances, and higher shunt resistances. Final I-V results indicated that the cell using tandem ETM layer, composite HTM layer, and single perovskite layer reports relatively more suitable performances. The optimization process employed in this study lead to a 1% efficiency increment.

Perovskite solar cells

Efficiency

Recombination

SCAPS-1D

Optimization

Configuration

The Perovskite Solar Cell (PSC) category, a branch of developing renewable energies instead of traditional expensive non-renewable ones, was introduced in 2009 (Kojima et al. 2009). They presented relatively acceptable performances until now. However, regarding their stability and environmental toxicity problems, they are not commercialized yet. PSCs’ operation mechanisms are based on a perovskite-based absorber layer that generates electrons and holes leading to electricity generation. The general formula of the perovskite layer is ABX₃, in which A, B, and X refer to monovalent cation, divalent cation, and halide ions, respectively (Hussain et al. 2018). Hundreds of perovskites formula can be formed regarding mentioned ions' vast variety. However, in the early years of the PSCs study, researchers usually employed methyl-ammonium lead-based perovskites regarding their significantly higher performances (Lee et al. 2014). Employing these PSC structures led to hazardous environmental impacts which resulted in stability problems, too (Babayigit et al. 2013a, 2013b; Serrano-Lujan et al. 2015). This created the motivation for studying the lead-free PSCs again (Duha and Borunda 2022).

The monovalent cation mentioned in the perovskite formula (A) can include Alkaline elements, some organic ions, etc. (Hussain et al. 2018). The mentioned divalent cations (B) are usually in group IV elements format such as lead, tin, germanium, etc. (Kirshnamoorthy et al. 2015). The halide ions refer to halogens as it is obvious from its name.

In addition to the perovskite absorber layer, other layers must be present in a PSC structure. They contain charge transporting and charge collection layers. Regarding two different charges that are generated from the absorber, including electrons and holes, the mentioned residual layers except the absorber layer are electron transporting material (ETM), hole transporting material (HTM), metal electrode (Anode), and transparent glass electrode (Cathode). In a PSC structure, the absorber layer, ETM, and HTM layers are the active layers of a PSC. Thus, they are more important

relative to the electrodes and the innovations of research around PSCs usually are based on active layers variation. HTM and ETM layers possess many types of organic and inorganic materials, too (Hussain et al. 2018). Therefore, generally, thousands of PSC structures can be formed. As a summary, some of the most important ions and materials of the active layers of a PSC are listed in Table 1. Moreover, Fig. 1. was given to represent a schematic view of a PSC structure.

Table 1

A summary of constituting materials of PSCs' active layers
Active layer	Ion/Material Category	Examples	descriptions	Reference
Perovskite	Monovalent Cation	Methylammonium (MA): CH₃NH₃+	Simple synthesis	(Hussain et al. 2018)
		Methylammonium (MA): CH₃NH₃+	and layer deposition, high efficiency	(Hussain et al. 2018)
		Formamidinium (FA): HC(NH₂)2+	Having a larger	(Aharon et al. 2014; Mei et al. 2014)
			lattice, more optimal bandgap, thermally more
			stable
		Cesium (Cs+)	Excellent thermal	(Correa-Baena et al. 2017)
			stability, does not possess an ideal
			bandgap
	Divalent Cation	Lead (Pb2+)	Excellent	(Hussain et al. 2018)
		Lead (Pb2+)	performances, environmental toxicity	(Hussain et al. 2018)
		Tin (Sn2+)	Does not have ecotoxicity, self- doping and	(Hussain et al. 2018)
		Tin (Sn2+)	oxidation problems	(Hussain et al. 2018)
		Germanium (Ge2+)	Very low	(Cui et al. 2015; Kopacic et al. 2018)
		Germanium (Ge2+)	efficiencies	(Cui et al. 2015; Kopacic et al. 2018)
	Halide ion	Iodine (I-)	More optimal	(Suarez et al. 2014; Seo et al. 2016)
			bandgap, suitable for single bandgap
			absorbers
		Chlorine (Cl-)	Possessing the	(Suarez et al. 2014; Seo et al. 2016)
			wider bandgap,
			more useful for
			light-emitting
			devices

		Bromine (Br-)	Suitable for tandem applications	(Suarez et al. 2014; Seo et al. 2016)
Electron Transporting Material	Inorganic	Transition metal oxides (TiO₂, ZnO, SnO₂, WO₃,etc.)	Most applicable, more efficiency	(Hussain et al. 2018)
Electron Transporting Material	Organic	Fullerenes and their derivatives (PCBM, C60)	good band gap alignment, sufficient electron mobility	(Hussain et al. 2018)
Hole Transporting Material	Small-molecules	Spiro-OMeTAD	relatively simple processing and extensive modifiability	(Hussain et al. 2018)
	Polymers	PEDOT:PSS, P3HT, PTAA, PANI	Cheaper, has excellent film- forming properties	(Bi et al. 2013)
	Inorganic	CuSCN, Cu₂O	high stability, high internal charge mobility, and generally low cost	(Conings et al. 2014)

Like all other solar cell types studies, PSCs study can be accomplished in both experimental and simulation phases (Hosseini et al. 2022). Simulation can give suitable information about the effecting parameters of developing a solar cell. It helps the experimental phase to concentrate on a custom study. For instance, regarding the rapid optimization process that can be performed in solar cell simulation software, by the optimization of a custom layer's thickness, the experiments can be accomplished around the obtained optimized values.

In recent years, several simulation studies were investigated around PSCs using solar cell simulation software. For example, in (Sharma et al. 2022) validated their experimental works with their simulation results using the SCAPS-1D simulation tool. They employed a polymer HTM with the CH₃NH₃PbI₃ absorber layer in their investigations. Their results represented an efficiency of around 28%. Moreover, they optimized the HTM layer's thickness and revealed that enhancing HTM thickness has a reverse impact on the cell's performance. In another study in 2022, a numerical simulation was accomplished by Belarbi et al. on a similar PSC structure (Belarbi et al. 2022). They investigated the effect of layer thicknesses, cell temperature, and series resistance on the photovoltaic parameters of the cell. Their optimal results demonstrate an efficiency of about 20%. In another simulation study, in (Abena et al. 2022) employed the SCAPS-1D package to compare various inorganic HTM layers' performance with Spiro-OMeTAD results to check if they are suitable substitutions or not. Results represented that the Cu2O layer as a HTM revealed a very good efficiency of about 27% and therefore it could be employed as a suitable HTM layer.

Inspired by the results and studies of previous simulation works, in the present research, our results will be presented in four different phases. In the first step, considering 8 different Methylammonium-based and Formamidinuim-based absorbers, three different organic and inorganic HTMs, and three different ETMs that conclude 72 unique PSC structures, the

photovoltaic parameters of each structure will be reported and optimal results will be collected. In the next steps, based on obtained optimal PSC structures, the layer properties, working points, and PSC structure configurations will be optimized, too. In the final part, the generally obtained I-V results will be reported in the forms of Tables and also, I-V curves.

To obtain the goals mentioned in the previous section, the SCAPS-1D (Solar Cell Capacitance Simulator- one-dimensional) simulation package is considered (Burgelman et al. 2016). This software was first developed by Marc Burgelman et al. at the Department of Electronics and Information Systems (ELIS) of the University of Gent, Belgium (Burgelman et al. 1996). This simulation tool can simulate a maximum of seven layers of a solar cell. Results of SCAPS-1D simulation can give information on current- voltage (I-V), capacitance-voltage (C-V), capacitance-frequency (C-f), Quantum efficiency(QE), and generation-recombination plots. The simulation is based on solving solar cells’ electron and hole continuity equations and Poisson’s equation, simultaneously (Burgelman et al. 2000). According to the mentioned equations and some other simulation custom settings, some primary properties should be given to the software to perform its simulation. These properties include the items given in Table 2–5 for the active layers and some other working points such as temperature, airmass, reflection in the front contact, series, and shunt resistances. The present study was performed at the temperature of 300 K, and A.M.1.5G airmass. The reflection and resistance will be discussed in the next sections. Table 2–5 represents the properties of the active layers for their different material candidates.

Table 2

electrical properties of methylammonium-based absorbers utilized in the study
Properties	MAPbI3 (Karimi and Ghorashi 2017a; 2017b)	MASnI3 (Zandi and Razaghi 2019; Reinders et al. 2017)	MASnBr3 (Reinders et al. 2017; Mandadapu et al. 2018)	MAPbI3-xClx (Reinders et al. 2017; Chowdhury et al. 2020)
Thickness (nm)	450	450	450	450
Bandgap (eV)	1.55	1.3	2.15	1.5
electron affinity (eV)	3.93	4.17	4.17	3.9
dielectric permittivity	6.5	8.2	10	10
CB effective density of states (1/cm3): N_C	1.66×1019	1×1018	2.5×1019	2.75×1018
VB effective density of states (1/cm3): N_V	5.41×1019	1×1018	2.5×1019	3.9×1018
electron thermal velocity (cm/s)	1×107	1×107	1×107	1×107
hole thermal velocity (cm/s)	1×107	1×107	1×107	1×107
electron mobility (cm²/Vs)	50	1.6	1.6	10
hole mobility (cm²/Vs)	50	1.6	1.6	10
donor density N_D (1/cm3)	0	0	0	0
acceptor density N_A (1/cm3)	3×1016	3×1016	3.2×1015	3×1016
defect density N_t (1/cm3)	1×1013	1×1013	1×1013	1×1013
Radiative recombination coefficient (cm³/s)	4.17×10 − 9	1.33×10 − 9	1.25×10 − 8	4.17×10 − 9
Auger electron capture coefficient (cm6/s)	1.39×10–25	4.44×10–26	3.91×10–24	1.39×10–25
Auger hole capture coefficient (cm6/s)	1.39×10–25	4.44×10–26	3.91×10–24	1.39×10–25

Table 3

electrical properties of formamidinuim-based absorbers utilized in the study
Properties	FASnI3 (Reinders et al. 2017; Abdelaziz et al. 2020)	FAPbI3 (Reinders et al. 2017; Karthick et al. 2020)	FA0.85Cs0.15PbI3 (Reinders et al. 2017; Karthick et al. 2020)	FA0.85Cs0.15Pb(I0.85Br0.15)3 (Reinders et al. 2017; Karthick et al. 2020)
Thickness (nm)	450	450	450	450
Bandgap (eV)	1.41	1.51	1.55	1.59
electron affinity (eV)	3.52	4	3.95	4.09
dielectric permittivity	8.2	6.6	6.6	6.6
CB effective density of states (1/cm3): N_C	1×1018	1.2×1019	2×1019	2×1019
VB effective density of states (1/cm3): N_V	1×1018	2.9×1018	2×1018	2×1018
electron thermal velocity (cm/s)	1×107	1×107	1×107	1×107
hole thermal velocity (cm/s)	1×107	1×107	1×107	1×107
electron mobility (cm²/Vs)	22	27	0.28	8.12
hole mobility (cm²/Vs)	22	18	2	2
donor density N_D (1/cm3)	0	0	1.3×1016	1.3×1016
acceptor density N_A (1/cm3)	7×1016	1.3×1016	1.3×1016	1.3×1016
defect density N_t (1/cm3)	1×1013	1×1013	1×1013	1×1013
Radiative recombination coefficient (cm³/s)	2×10–10	3.85×10 − 9	3.85×10 − 9	3.85×10 − 9
Auger electron capture coefficient (cm6/s)	9.3×10–30	2.96×10–25	2.96×10–25	2.96×10–25
Auger hole capture coefficient (cm6/s)	9.3×10–30	2.96×10–25	2.96×10–25	2.96×10–25

Table 4

electrical properties of electron transporting layers’ materials utilized in the study (Karimi and Ghorashi 2017a; 2017b; Zandi and Razaghi 2019; Reinders et al. 2017).
Properties	TiO2	ZnO	PCBM
Thickness (nm)	100	100	100
Bandgap (eV)	3.2	3.3	1.8
electron affinity (eV)	3.9	4.6	3.9
dielectric permittivity	9	9	7
CB effective density of states (1/cm3): N_C	1×1019	2.2×1018	1.5×1018
VB effective density of states (1/cm3): N_V	1×1019	1.8×1019	2.4×1019
electron thermal velocity (cm/s)	1×107	1×107	1×107
hole thermal velocity (cm/s)	1×107	1×107	1×107
electron mobility (cm²/Vs)	20	100	80
hole mobility (cm²/Vs)	10	25	4
donor density N_D (1/cm3)	1×1016	1×1018	2×1017
acceptor density N_A (1/cm3)	0	0	0
defect density N_t (1/cm3)	0	0	0
Radiative recombination coefficient (cm³/s)	2×10 − 8	3.33×10 − 7	2.78×10 − 9
Auger electron capture coefficient (cm6/s)	2×10–24	3.33×10–25	1.39×10–26
Auger hole capture coefficient (cm6/s)	2×10–24	3.33×10–25	1.39×10–26

Table 5

electrical properties of hole transporting layers’ materials utilized in the study (Karimi and Ghorashi 2017a; 2017b; Zandi and Razaghi 2019; Reinders et al. 2017).
Properties	Spiro- OMeTAD	PEDOT:PSS	CuSCN
Thickness (nm)	200	200	200
Bandgap (eV)	3.06	1.5	3.6
electron affinity (eV)	2.05	3.6	1.7
dielectric permittivity	3	10	1
CB effective density of states (1/cm3): N_C	2.8×1019	1×1021	2.2×1019
VB effective density of states (1/cm3): N_V	1×1019	1×1021	1.8×1018
electron thermal velocity (cm/s)	1×107	1×107	1×107
hole thermal velocity (cm/s)	1×107	1×107	1×107
electron mobility (cm²/Vs)	1×10 − 4	1	100
hole mobility (cm²/Vs)	2×10 − 4	40	25
donor density N_D (1/cm3)	0	0	0
acceptor density N_A (1/cm3)	1×1018	1×1019	1×1018
defect density N_t (1/cm3)	0	0	0
Radiative recombination coefficient (cm³/s)	1×10 − 8	5.5 ×10–11	2×10–10
Auger electron capture coefficient (cm6/s)	1×10–26	5.56×10–30	2×10–28
Auger hole capture coefficient (cm6/s)	1×10–26	5.56×10–30	2×10–28

As can be seen from Table 2–5, different organic and inorganic types of ions and materials are considered for each layer to investigate their performance. Moreover, for all of the simulations performed in this study, the gold (Au) and Fluoridized tin-oxide (FTO) are considered as the metal contact and front contact, respectively. The SCAPS-1D required information for the mentioned electrodes is their work function which is 4.4 eV and 5.1 eV for FTO and Au layers, respectively. As it was mentioned previously, in the present study, firstly the optimum PSC structures among 72 ones were selected. After that, different factors were optimized using the SCAPS-1D ‘’batch calculation’’ and ‘’recorder calculation’’ parts.

The simulation was performed according to the mentioned simulation conditions in the previous sections. However, as mentioned earlier, the present work is based on four different phases. In the following, the results of mentioned phases will be presented.

3.1. PSC structure selection

According to Table 1 descriptions, for a perovskite material in PSCs, the MA+, FA+, Cs+, Pb2+, Sn2+, I-, Br-, and Cl- ions are the most employed and high-efficient candidates. Therefore, using the literature data mentioned in Table 2 and Table 3, we considered 8 different absorbers to investigate the performance of each of the mentioned ions. The absorbers contain MAPbI3, MASnI3,

MASnBr₃, MAPbI_3-xCl_x, FASnI₃, FAPbI₃, FA_0.85Cs_0.15PbI₃, FA_0.85Cs_0.15Pb(I_0.85Br_0.15)₃

layers. Moreover, according to Table 1 descriptions and also the classification of HTM and ETM layers mentioned in the introduction part, we considered 3 different layers for both ETM and HTM layers. They include TiO₂, ZnO, and PCBM materials for the ETM layer, and Spiro-OMeTAD, PEDOT:PSS, and CuSCN materials for the HTM layer. This leads to the formation of 72 unique

PSC structures. In the first phase, utilizing SCAPS-1D batch study options, the performance parameters of these PSC structures were investigated. The results are given in Table 6. However, this Table only indicates a small part of the complete results that are presented in Table SA1 (supplementary data A). Data from Table SA1 are categorized into four different statuses including good results (green color, efficiency higher than 20%), normal results (yellow color, efficiency between 10% and 20%), bad results (efficiency lower than 10%), and illogical results (purple color, non-logical or out of range results). Table 6 represents a small mixture of good, normal, and bad results. However, the good results are collected in Table 7, too. The other phases of the present study will be based on good results and the data optimization will be performed on Table 7 PSC structures.

Table 6

performance parameters of different PSC structures considered in the present study
HTM	ETM	Perovskite (Absorber)	PCE (%)	JSC (mA/cm2)	VOC (V)	FF (%)
CuSCN	PCBM	FA0.85Cs0.15PbI3	15.63	17.97	1.06	82.16
CuSCN	PCBM	FASnI₃	20.66	23.63	1.10	79.28
CuSCN	TiO₂	FA0.85Cs0.15PbI3	20.41	23.07	1.09	81.21
CuSCN	TiO₂	FAPbI₃	16.38	23.22	1.04	67.54
CuSCN	TiO₂	FASnI₃	26.03	27.75	1.13	83.16
CuSCN	ZnO	MASnI₃	16.14	28.35	0.94	60.41
PEDOT:PSS	PCBM	FA0.85Cs0.15Pb(I0.85Br0.15)3	9.14	16.83	0.85	63.83
PEDOT:PSS	PCBM	FA0.85Cs0.15PbI3	12.33	18.44	0.90	74.66
PEDOT:PSS	PCBM	MAPbI_3-xCl_x	13.26	17.27	1.02	75.21
PEDOT:PSS	PCBM	MASnBr₃	3.54	7.18	0.85	57.72
PEDOT:PSS	PCBM	MASnI₃	15.53	25.38	0.90	67.85
PEDOT:PSS	TiO₂	FAPbI₃	13.34	23.08	0.92	62.56
PEDOT:PSS	TiO₂	MASnBr₃	4.66	12.57	0.89	41.79
PEDOT:PSS	TiO₂	MASnI₃	10.80	26.81	0.92	43.74
PEDOT:PSS	ZnO	FA0.85Cs0.15PbI3	9.93	23.68	1.10	38.24
Spiro- OMeTAD	PCBM	FA0.85Cs0.15Pb(I0.85Br0.15)3	9.59	16.13	1.02	58.54
Spiro- OMeTAD	PCBM	FA0.85Cs0.15PbI3	12.94	17.87	1.04	69.50
Spiro- OMeTAD	PCBM	FASnI₃	20.28	23.61	1.10	77.88

Spiro- OMeTAD	PCBM	MAPbI_3-xCl_x	15.87	19.69	1.04	77.16
Spiro- OMeTAD	PCBM	MASnBr₃	2.51	4.52	0.94	58.71
Spiro- OMeTAD	TiO₂	FA0.85Cs0.15PbI3	17.16	23.05	1.07	69.55
Spiro- OMeTAD	TiO₂	FASnI₃	25.44	27.75	1.13	81.30
Spiro- OMeTAD	TiO₂	MAPbI₃	16.69	22.58	0.99	74.48
Spiro- OMeTAD	TiO₂	MASnBr₃	4.18	9.26	0.93	48.78
Spiro- OMeTAD	ZnO	FASnI₃	12.63	27.63	1.02	44.72
Spiro- OMeTAD	ZnO	MASnBr₃	4.23	9.13	0.93	49.96

According to Table 6, and completely Table SA1, the following items can be concluded:

In the case of HTM materials, the CuSCN-based and PEDOT:PSS-based PSC structures represent the best and the worst efficiency average relative to the others, respectively.
In the case of ETM materials, the TiO2-based and ZnO-based PSC structures represent the best and the worst efficiency average relative to the others, respectively.
In the case of monovalent cations, the formamidinuim-based PSC structures represent better efficiency averages relative to the others. Moreover, all of the good results are based on FA- based PSCs.
In the case of divalent cations, the lead-based PSC structures represent a better efficiency average relative to the tin-based ones. Nevertheless, most of the good results are based on Sn- based PSCs.
In the case of halide ions, the iodine-based PSC structures represent better efficiency averages relative to others. Moreover, iodine/chlorine-mixed-based ones represented acceptable results.

On the other side, bromine-based PSCs indicated significantly bad performances (an average of about 4% efficiency)

Regarding the mentioned average reports obtained from Table 6 and Table SA1, a PSC structure with the ETM of TiO₂, HTM of CuSCN, and the absorber of FAPbI₃ is suggested. However, for the validation of obtained suggested PSC structure, the good results (efficiency higher than 20%) can help.

Table 7

optimum obtained PSC structures
Structure Code	HTM	ETM	Perovskite (Absorber)	PCE (%)	JSC (mA/cm2)	VOC (V)	FF (%)
S1	CuSCN	TiO₂	FA0.85Cs0.15PbI3	20.41	23.07	1.09	81.21
S2	CuSCN	PCBM	FASnI₃	20.66	23.63	1.10	79.28
S3	CuSCN	TiO₂	FASnI₃	26.03	27.75	1.13	83.16
S4	Spiro- OMeTAD	PCBM	FASnI₃	20.28	23.61	1.10	77.88
S5	Spiro- OMeTAD	TiO₂	FASnI₃	25.44	27.75	1.13	81.30

According to Table 7 results, it can be seen that in the case of ETM and HTM, two optimum PSC structures match the proposed average PSC structure. However, the obtained absorbers in Table 7 differ from the suggested structure. Besides, From Table 6 it can be seen that the proposed CuSCN/FAPbI₃/TiO₂ PSC structure reveals an efficiency of 16.38% and have about 4% deviation from the good results boundary. However, as an average proposed structure, this difference is almost acceptable.

Furthermore, from Table 7, it can be concluded that:

In the case of HTMs, CuSCN-based cells possess the highest contribution among other HTM candidates. Moreover, PEDOT:PSS-based cells have no portion in the good results.
In the case of ETMs, TiO₂-based cells possess the highest contribution among other ETM candidates. Moreover, ZnO-based cells have no portion in the good results.
In the case of absorbers, FASnI₃-based structures have significantly more portions than the others and except FA_0.85Cs_0.15PbI₃-based PSCs, no other absorbers give an efficiency higher than 20%.

3.2. Layer property optimization

In the second phase, the optimum PSC structures mentioned in Table 7 as the candidates of all PSCs, are considered to be optimized from the layer properties point of view. For this, the effect of variation of important parameters of active layers including their thickness and doping density is investigated. In the following, the photovoltaic parameters and recombination factor’s variation will be represented as the functions of layer properties for S1-S5 structures (Table 7). The photovoltaic parameters refer to the performance factors that are given in Table 6–7 and Table SA1. Moreover, the recombination factor refers to the recombination peak created in recombination path plots of each property value. It should be noted that regarding the numerous data and diagrams for each structure, only S3 structure results will be reported here and the rest are given in Supplementary Data B. More discussion will be performed in the following.

3.2.1. Perovskite thickness

Employing the SCAPS-1D batch study, the effect of perovskite thickness variation was investigated. For this section, the effect of perovskite thickness variation from 100 nm-1000 nm on the photovoltaic parameters and recombination factor are given in the diagrams of Fig. 2 for S3, and Figure SB2-SB5 for S1, S2, S4, and S5 structures. Moreover, the recombination paths of

different absorber thicknesses are given in Figure SB1. (This plot is only given for the perovskite thickness variation and the other recombination path plots are similar to it. Hence, we didn’t present them). As mentioned earlier, the recombination factors that were reported in Fig. 2 and Figure SB2-SB5, are the peaks of the Figure SB1 diagram.

It can be seen from Figure 2 that perovskite thickness increment has a generally positive impact on the performance parameters. Nevertheless, the recombination rate peak will also enhance. This can be justified according to the literature (Bag et al. 2020). As the perovskite thickness increase, the electron- hole generation will enhance, too. On the other side, their recombination probability will raise.

The V_OC and J_SC refer to the charge’s recombination and generation, respectively. This justifies the behaviors of Figure 2 and Figure SB2-SB5 diagrams. According to the efficiency plot of Figure 2, the efficiency increment rate decreases after the thickness of 500 nm. This helps in selecting the proper thickness regarding the recombination enhancement on the other side. Hence, it seems that the thickness of 500 nm can be an optimum selection for the perovskite thickness.

3.2.2 ETM thickness

Similar to the previous section’s diagrams, the effect of ETM thickness variation was investigated in the thickness range of 40 nm-140 nm. The results are reported in the diagrams of Figure 3 for S3 and Figure SB6-SB9 for S1, S2, S4, and S5 structures.

It can be understood from Figure 3 diagrams’ behavior that enhancing the ETM thickness has a generally negative impact on the performance of PSC. This can be attributed to the late transportation between the absorber and front contact due to the wider distance (Jeyakumar et al. 2020). Moreover, the recombination rate will increase by the thickness enhancement. Figure SB7-SB9 present a similar behavior to Figure 3 and the mentioned justifications can be also applied to them. However, the behavior is completely reversed for the S1 PSC structure demonstrated in Figure SB6. This is certainly because of the difference in absorber between the mentioned optimum structures. However, in the case of S2-S5 structures, according to the more efficiency and less recombination in the thickness of 40 nm, this thickness can be suitable for the optimum selection. This is completely reversed for the S1. Therefore, the thickness of 140 nm was selected.

3.2.3 HTM thickness

Similar to the previous investigations, the HTM thickness effect is considered. For that, the thickness range of 50 nm-400 nm was selected. The results are given in the formats of Figure 4 diagrams for S3 and Figure SB10-SB13 diagrams for S1, S2, S4, and S5 PSC structures.

Despite considerable changes in the previous investigations, as can be seen from Figure 4 and Figure SB10-SB13, no significant changes occur in the performance parameters of PSCs in the considered thickness range of HTMs. It should be noted that despite the ETM layer, the HTM layer does not affect the quality of light absorption by the perovskite layer due to the configurations of PSCs. In other words, light crosses over the ETMs, but it is absorbed by the perovskite layer before reaching the HTM layer. Therefore, electron-hole generation is not influenced by the variation in HTM thickness (Hosseini et al. 2022).

As a summary, the optimum selected thicknesses for the active layers of S1-S5 PSC structures were collected in Table 8.

Table 8- A summary of obtained optimum thicknesses for the active layers of S1-S5 structures

Structure	Perovskite optimum thickness	ETM optimum thickness	HTM optimum thickness
S1	140 nm	400 nm	50 nm
S2	80 nm	600 nm	50 nm
S3	40 nm	500 nm	50 nm
S4	80 nm	600 nm	50 nm
S5	40 nm	300 nm	50 nm

3.2.4 Perovskite doping

The doping density of a layer is another important parameter of a material. It refers to the concentration of impurities in a material. These impurities can be in acceptor or donor types for positive and negative doping, respectively. Hence, the value of doping density has a direct relation with the charge concentration on a material. Therefore, its value will affect the cell’s performance, too.

In the present study, the effect of perovskite doping density was investigated in the doping density range of 1017-1021 1/cm3 for the S3 structure. Figure 5 demonstrates the results in the formats of performance and recombination parameters. A similar task was performed for S2, S4, and S5 structures as indicated in Figure SB14-SB16. (As can be seen in Table 3, the FA0.85Cs0.15PbI3 perovskite layer is an inert material and has equal donor and acceptor densities. Hence, we preferred not to investigate it)

The diagrams of Figure 5 and Figure SB14-SB16 reveal almost similar behaviors with the variation of doping density. The diagrams represent the descending behavior for the performance parameters of the considered structures. This can be attributed to the recombination increment. Enhancing the doping density of the absorber layer helps more generation of charge carriers. However, after a threshold value, it leads to more charge recombination, too. Regarding the obtained results, it seems that the lowest range density is the optimum value for all of the S1-S5 PSC structures utilized in this work.

3.2.5 ETM doping

Similar to the perovskite doping variation, it is better to find the optimum doping densities of transporting layers, too. In this case, the effect of ETM doping variation was investigated in the range of 1015-1022 1/cm3. The results were represented in the forms of Figure 6 diagrams for S3 and Figure SB17-SB20 diagrams for S1, S2, S4, and S5 PSC structures.

According to the diagrams indicated in Figure 6 and Figure SB17-SB20, it can be seen that each of the structures represents different behavior than the others. Therefore, we can’t find any certain behavior for them. However, this uncertainty may have different reasons. It could be due to the SCAPS-1D calculation error in some special doping densities. The difference in active layers among the structures may be another justification for it. However, in the case of the S3 structure, according to the efficiency and recombination plots, we selected the donor density of 1017 1/cm3.

3.2.6 HTM doping

HTM doping is one of the other crucial parameters that affect PSC performance. Regarding the concept that HTM doesn't need to be transparent, the higher values of its doping density can lead to the better transportation of holes to the metal contact. The results of HTM doping density variation in the range of 1015-1022 1/cm3 confirm this subject. The results were reported in Figure 7 diagrams for S3 and Figure SB21-SB24 for S1, S2, S4, and S5 PSC structures.

It is obvious from the mentioned diagrams that, the performance curves of S1-S5 PSC structures possess a generally increasing behavior. On the other side, the recombination plots represent a similar behavior, too. Hence, a decision should be accomplished for the selection of proper doping density for HTM. In the case of S3, it can be seen that after a threshold value, the efficiency doesn’t promote. Therefore, it seems that the value of 1017 1/cm3 can be a suitable choice for HTM doping density. To have a better insight, the optimum values of S1-S5 structures for doping densities of active layers were summarized in Table 9.

Table 9- A summary of obtained optimum doping densities for the active layers of S1-S5 structures

Structure	Perovskite optimum doping density	ETM optimum doping density	HTM optimum doping density
S1	1.3×1016 1/cm3	1016 1/cm3	1015 1/cm3
S2	1016 1/cm3	1017 1/cm3	1018 1/cm3
S3	1017 1/cm3	1017 1/cm3	1017 1/cm3
S4	1017 1/cm3	1017 1/cm3	1018 1/cm3
S5	1017 1/cm3	1017 1/cm3	1018 1/cm3

3.3 Working point optimization

In addition to the mentioned layer parameters that were investigated, some simulation general conditions should be considered regarding their importance. In the following, some of these general conditions will be discussed.

3.3.1 Temperature

Solar cells’ temperature is one of the crucial conditions that should be calibrated properly. Its optimization can help the experimental studies to be performed in more suitable situations. It seems that the simulation process can be employed well in this case. Therefore, in the present case, we selected the temperature range of 300-400 K to find an optimal temperature. The results of Figure 8 for S3 and Figure SB25-SB28 for S1, S2, S4, and S5 indicate the temperature’s effect on the performance parameters of PSCs.

According to the mentioned results of S1-S5 structures, it is obvious that a generally descending behavior can be seen by the increment of temperature. Therefore, selecting the lower temperatures will lead to better performance results. We considered the temperature of 300 K for all of the studied structures. This can be attributed to the higher mobility of charge carriers in high temperatures that lead to more heat dissipation and lower performance.

3.3.2 Series resistance

Solar cells’ operation mechanisms suffer from parasitic resistances existing in their circuits. These resistances include series and shunt ones. They relate to the electrons and holes generation and recombination mechanisms. Generally, series resistance and shunt resistance represent a cell’s resistance to the generation and recombination processes, respectively (Burgelman et al. 2016; Diantoro et al. 2018). The lower and higher values will be the optimum amounts for the series and shunt resistances, respectively. However, in this case, the effect of series resistance was investigated in the range of 0.1-1 Ω.cm2. The results are demonstrated in Figure 9 for S3 and Figure SB29-SB32 for S1, S2, S4, and S5 PSC structures. It should be noted that the mentioned resistances don’t have any special effects on the recombination. Therefore, we preferred not to report them.

As mentioned in the last paragraph, the lower values would be desirable for series resistances. The results of Figure 9 and Figure SB29-SB32 confirm this subject, too.

3.3.3 Shunt resistance

As mentioned earlier, the higher values of shunt resistances are desirable for a solar cell. As the shunt resistance enhances, the cell will approximate more to the ideal solar cell with infinite shunt resistance. To confirm this concept, we considered a relatively vast data range of 102-105 Ω.cm2 for the investigation of the shunt resistance effect. However, after a threshold value, the increment will not be anymore. The diagrams of Figure 10 for S3 and Figure SB33-SB37 for S1, S2, S4, and S5 confirm this subject, too.

To have a better insight, the results of resistances’ optimization were collected in Table 10 for S1-S5 PSC structures.

Table 10- A summary of obtained optimum series and shunt resistances for the active layers of S1-S5 structures

PSC structure	Optimum series resistance (Ω.cm2)	Optimum shunt resistance (Ω.cm2)
S1	0.1	2×104
S2	0.1	2×104
S3	0.1	2×104
S4	0.1	104
S5	0.1	5×103

3.3.4 Reflection

In most simulation studies, the reflection parameters are not consumed and the calibrations are far from the real conditions. However, in SCAPS-1D this parameter can be considered, too. The reflection parameter shows the percentage of transmitted light to the absorber layer. It is clear that by the reflection increment, the amount of absorbed light will be decreased. Therefore, it leads to lower performances. The diagrams of Figure 11 for S3 and Figure SB37-SB40 for S1, S2, S4, and S5 PSC structures confirm this. However, on the other side, the lower electron-hole generation can lead to lower recombination, too. Therefore, as represented in the mentioned diagrams, a decrement occurs for recombination plots. Hence, selecting the optimum reflection depends on the object of the study that considers higher performances or lower recombination.

3.4 Configuration optimization

In addition to the mentioned effective factors that should be optimized, the configuration and number of layers in a PSC structure can significantly affect the performance of the cell. Each active layer of a PSC can be configured in single, tandem, or composite forms. So far, all of the investigated parts of the present study are performed in single configurations of the active layers. In other words, only a single material was used for each of the active layers. However, in some cases, more than one material can be employed for each layer. This sometimes can lead to a better performance. In the case of utilizing more than one material for a custom active layer (often two materials), the mentioned layer should be configured in the composite or tandem formats. In the following, the mentioned configurations will be discussed more.

3.4.1 Tandem PSCs

Tandem configuration is one of the common arrangements of PSCs when using more than one material for an active layer. In the case of two different materials for a layer, two various tandem configurations can be formed depending on the placement order of the materials. In the present study, according to the optimum structures’ materials referred to in Table 7, the tandem configuration simulation was accomplished twice for each of the active layers. Depending on the placement order, we named unique abbreviations for each of the active layers' tandem configurations. They include TP1 and TP2 for the perovskite layer, TE1 and TE2 for the ETM layer, and TH1 and TH2 for the HTM layer. Their corresponding structure formation were given in Table 11 with their results of simulation (photovoltaic parameters). It should be noted that each of the tandem layers’ thickness was selected as the half value of the single active layer.

Table 11- photovoltaic parameters upon using tandem structures in active layers

Structure code	Structure formulation	V_OC (V)	JSC (mA/cm2)	FF (%)	PCE (%)
TP1	Au/CuSCN/ FASnI₃/ FA_0.85Cs_0.15PbI₃/TiO₂/FTO	1.08	26.66	61.49	17.69
TP2	Au/CuSCN/FA_0.85Cs_0.15PbI₃/ FASnI₃/TiO₂/FTO	1.04	3.24	82.12	2.76
TE1	Au/CuSCN/ FASnI₃/ PCBM/TiO₂/FTO	1.13	28.26	84.58	26.93
TE2	Au/CuSCN/ FASnI₃/ TiO₂/ PCBM/ FTO	1.12	26.43	83.16	24.57
TH1	Au/CuSCN/Spiro- OMeTAD/FASnI₃/TiO₂/FTO	1.13	28.23	83.51	26.56
TH2	Au/Spiro-OMeTAD/ CuSCN/FASnI₃/TiO₂/FTO	1.13	28.23	83.64	26.60

According to the results of the tandem structures' simulation, it can be seen that employing two absorbers in the tandem format has a negative impact on the performance of the cell. For instance, the efficiency results of TP1 and TP2 structures are considerably lower than their similar single S1 and S3 PSC structures. In the case of ETM, it can be seen from Table 7 that TE1 and TE2 structures are the mixture formats of single S2 and S3 structures. The efficiency results reveal that the TE1 structure possesses the most efficiency among the mentioned structures. Moreover, the efficiency value of TE2 is between S2 and S3 structures’ efficiencies. Therefore, it seems that choosing the TE1 tandem structure will be a proper selection. Similar to the ETM tandem selection, the results of HTM tandems represent better efficiencies for both TH1 and TH2 tandem structures relative to their corresponding single structures (S3 and S5). However, regarding a little difference, we can select the TH2 structure as the optimum one.

It should be noted that the reported results were given in only a custom thickness for the tandem layers. For more comparison, it is better to consider more range of thicknesses for the studied tandem layers. For that, a more complete study was accomplished using the batch simulation of SCAPS-1D for different thicknesses of the active layers. The results are collected in Table 12-14 for perovskite, ETM, and HTM layers. The thickness range was considered 100-500 nm for each tandem perovskite, 20-100 nm for each tandem ETM, and 50-250 nm for each tandem HTM.

Table 12- photovoltaic parameters upon using different thicknesses of tandem absorbers

FASnI₃ thickness (nm)	FA0.85Cs0.15PbI3 thickness (nm)	JSC (mA/cm2)	V_OC (V)	FF (%)	PCE (%)	Recombination rate peak (1/cm3)
100	100	19.36	1.09	67.24	14.13	5.90×1018
100	200	22.29	1.07	63.20	15.10	4.41×1018
100	300	24.01	1.06	60.76	15.51	3.28×1018
100	400	25.06	1.06	58.94	15.61	2.56×1018
100	500	25.74	1.05	57.41	15.57	2.11×1018
200	100	23.74	1.09	67.11	17.44	1.84×1019
200	200	25.26	1.08	62.92	17.20	1.34×1019
200	300	26.19	1.07	60.36	16.97	1.01×1019
200	400	26.79	1.07	58.48	16.73	8.00×1018
200	500	27.18	1.07	56.93	16.48	6.71×1018
300	100	26.24	1.10	67.20	19.33	3.12×1019
300	200	27.07	1.08	62.93	18.47	2.29×1019
300	300	27.60	1.08	60.31	17.92	1.76×1019
300	400	27.95	1.07	58.37	17.49	1.43×1019
300	500	28.18	1.07	56.78	17.11	1.22×1019
400	100	27.72	1.10	67.32	20.44	4.21×1019
400	200	28.20	1.08	63.02	19.27	3.15×1019
400	300	28.53	1.08	60.34	18.54	2.46×1019
400	400	28.74	1.07	58.38	18.00	2.04×1019
400	500	28.89	1.07	56.75	17.55	1.76×1019
500	100	28.62	1.09	67.43	21.11	5.08×1019
500	200	28.93	1.08	63.12	19.79	3.87×1019
500	300	29.15	1.08	60.41	18.96	3.08×1019
500	400	29.29	1.07	58.42	18.36	2.58×1019
500	500	29.39	1.07	56.78	17.87	2.26×1019

Table 12 represents the list of photovoltaic parameters and recombination factor values for different thicknesses of perovskite tandem materials. A simple look can give the highest efficiencies and the lowest recombination. However, the results do represent not any data with the highest efficiency and the lowest recombination, at the same time. For instance, the thicknesses of 500 nm for FASnI₃ and 100 nm for FA_0.85Cs_0.15PbI₃ reports the highest efficiency. However, it represents the highest recombination, too. In other data, the thicknesses of 100 nm for FASnI3 and 500 nm for FA_0.85Cs_0.15PbI₃ gives the lowest recombination, but it doesn’t possess a relatively suitable performance. Therefore, depending on the project object, the optimum result can be extracted.

Table 13-photovoltaic parameters upon using different thicknesses of tandem ETMs

PCBM thickness (nm)	TiO₂ thickness (nm)	JSC (mA/cm2)	VOC (V)	FF (%)	PCE (%)	Recombination rate peak (1/cm3)
20	20	28.29	1.13	84.66	26.97	5.64×1019
20	40	28.25	1.13	84.38	26.84	5.51×1019
20	60	28.22	1.13	84.21	26.76	5.32×1019
20	80	28.20	1.13	84.13	26.71	5.12×1019
20	100	28.18	1.13	84.09	26.67	5.07×1019
40	20	28.32	1.13	83.69	26.67	5.1×1019
40	40	28.29	1.12	83.26	26.50	4.87×1019
40	60	28.26	1.12	83.07	26.40	4.77×1019
40	80	28.22	1.12	83.02	26.35	4.76×1019
40	100	28.19	1.12	83.01	26.32	4.76×1019
60	20	28.37	1.12	81.90	26.12	4.58×1019
60	40	28.30	1.12	81.53	25.93	9.54×1019
60	60	28.25	1.12	81.36	25.82	1.27×1020
60	80	28.21	1.12	81.32	25.77	1.34×1020
60	100	28.18	1.12	81.31	25.74	1.36×1020
80	20	28.25	1.12	80.02	25.38	2.82×1020
80	40	28.14	1.12	79.72	25.18	4.12×1020
80	60	28.08	1.12	79.57	25.08	4.54×1020
80	80	28.04	1.12	79.54	25.03	4.63×1020
80	100	28.00	1.12	79.54	25.00	4.64×1020
100	20	27.89	1.12	78.37	24.52	7.32×1020
100	40	27.75	1.12	78.15	24.32	8.65×1020
100	60	27.68	1.12	78.04	24.22	9.05×1020
100	80	27.64	1.12	78.02	24.17	9.13×1020
100	100	27.61	1.12	78.02	24.14	9.13×1020

Selecting the proper thicknesses for tandem ETMs is not as difficult as in the perovskite case as can be seen from Table 13. The data represents the highest performance and almost the lowest efficiency for the 20 nm thicknesses for both of the tandem ETMs. Therefore, it can be a suitable choice for the selection.

Table 14-photovoltaic parameters upon using different thicknesses of tandem HTMs

CuSCN thickness (nm)	Spiro- OMeTAD	JSC (mA/cm2)	VOC (V)	FF (%)	PCE (%)	Recombination rate peak (1/cm3)
	thickness (nm)
50	50	28.23	1.13	83.53	26.57	6.12×1019
50	100	28.23	1.13	83.53	26.57	6.12×1019
50	150	28.23	1.13	83.53	26.57	6.12×1019
50	200	28.23	1.13	83.53	26.57	6.12×1019
50	250	28.23	1.13	83.53	26.57	6.12×1019
100	50	28.23	1.13	83.18	26.45	6.13×1019
100	100	28.23	1.13	83.18	26.45	6.13×1019
100	150	28.23	1.13	83.18	26.45	6.13×1019
100	200	28.23	1.13	83.18	26.45	6.13×1019
100	250	28.23	1.13	83.18	26.45	6.13×1019
150	50	28.23	1.13	82.83	26.34	6.14×1019
150	100	28.23	1.13	82.83	26.34	6.14×1019
150	150	28.23	1.13	82.83	26.34	6.14×1019
150	200	28.23	1.13	82.83	26.34	6.14×1019
150	250	28.23	1.13	82.83	26.34	6.14×1019
200	50	28.23	1.13	82.49	26.23	6.15×1019
200	100	28.23	1.13	82.49	26.23	6.15×1019
200	150	28.23	1.13	82.49	26.23	6.15×1019
200	200	28.23	1.13	82.49	26.23	6.15×1019
200	250	28.23	1.13	82.49	26.23	6.15×1019
250	50	28.23	1.13	82.14	26.12	6.16×1019
250	100	28.23	1.13	82.14	26.12	6.16×1019
250	150	28.23	1.13	82.14	26.12	6.16×1019
250	200	28.23	1.13	82.14	26.12	6.16×1019
250	250	28.23	1.13	82.14	26.12	6.16×1019

Similar to the HTM thickness variation effect investigation for single configurations, it can be seen from Table 14 that this also applies to the tandem arrangements. However, regarding a little difference, we can select the 50 nm thicknesses for both of the considered HTMs.

3.4.2 Composite PSCs

In addition to the simulation of simple planar PSC structures, SCAPS-1D has the capability of composite layers’ structure simulation. Similar to the tandem cases, the simulators can consider composite configurations for each layer. For example, in the case of the perovskite layer, themixture or composition of two perovskite materials (e.g., Table 7 materials) can be employed instead of a tandem arrangement. In the case of composite configurations, the composition of materials in the mixture is an important factor. Therefore, similar to the previous investigations, we considered the impact of active layers’ composition on the performance and recombination parameters of PSCs. The results were demonstrated in Figure 12-14 for the active layers.

As can be seen from Figure 12, enhancing the composition of FA_0.85Cs_0.15PbI₃ has a negative effect on the performance of FA_0.85Cs_0.15PbI₃/FASnI₃-based PSCs. Therefore, it seems that utilizing a single FASnI₃-based perovskite can give better performances and the composite configuration didn’t report acceptable results.

Similar to the perovskite composite results, the diagrams of the PCBM composition effect in the PCBM/TiO₂ mixture indicated in Figure 13, possess a generally descending behavior. Hence, selecting the low values for PCBM composition is desired. However, considering the little differences, we selected 25% composition for PCBM and 75% for TiO2.

According to Figure 14 diagrams, it can be seen that the Spiro-OMeTAD composition variation has not any significant effect on the performance parameters of Spiro-OMeTAD/CuSCN-based PSC. However, in the composition of 100% for Spiro-OMeTAD (single Spiro-OMeTAD), the results are significantly lower than the others. Generally, by very little difference, we selected 80% composition for Spiro-OMeTAD.

3.5 Final I-V results

To possess better insights, it is better to summarize the obtained results. For this, the photovoltaic parameters of different configurations from the primary run to the mixed composite-tandem structures were collected in Table 15. As a summary of previous investigation for S3 PSC structure, it seems that the single FASnI₃ configurations for perovskite, tandem arrangement for ETM, and composite structure for HTM reveal the optimum efficiency for each case. Therefore, we decided to consider all of them in a single simulation as the final step.

Table 15- photovoltaic parameters of different PSC configurations for S3 structure

Structure mode	Status	V_OC (V)	JSC (mA/cm2)	FF (%)	PCE (%)
single	Primary run	1.13	27.75	83.16	26.02
single	Results after property optimization	1.13	28.23	83.86	26.67
Tandem	Best perovskite tandem (TP1)	1.08	26.66	61.49	17.69
Tandem	Best ETM tandem (TE1)	1.13	28.26	84.58	26.93
Tandem	Best HTM tandem (TH2)	1.13	28.23	83.64	26.60
composite	Best ETM composite (0.75 TiO₂, 0.25 PCBM)	1.13	28.23	84.14	26.70
composite	Best HTM composite (0.8 Spiro-OMeTAD, 0.2 CuSCN)	1.13	28.23	84.14	26.76
Tandem/ composite	Best PSC structure (single perovskite, tandem ETM- TE1, composite HTM-0.8 Spiro- OMeTAD, 0.2 CuSCN)	1.13	28.29	84.94	27.07

According to Table 15 results, it can be seen that all of the optimization steps including layer property, work points, and configuration optimizations have a positive impact on the general performance of basic considered PSCs (S1-S5). The total efficiency was increased by about 1%. However, it should be noted that in this study the recombination factor was considered in addition to the performance factors during the optimization processes and this led to lower efficiency increments. To have a better insight, the total I-V curves of different processes are given in Figure 15.

The area under the I-V curve of a solar cell represents its performance. It is obvious from the given I-V plots that this area was increased by more progression of the optimization processes. This confirms the total positive process of the present work.

In summary, this study presented some theoretical investigations around perovskite solar cells simulation employing the SCAPS-1D simulation package. Regarding perovskite solar cells’ vast variety of structures that can be formed, this work was based on selecting proper PSCs among the proposed structures. For these 8 different MA-based and FA-based absorbers were considered. Moreover, some organic and inorganic materials including TiO₂, ZnO, and PCBM as ETMs, and

CuSCN, PEDOT:PSS, and Spiro-OMeTAD as HTMs were considered that formed 72 unique PSC structures. Among the mentioned PSCs, 5 structures were selected as good data (efficiency above 20%) for more investigation. For that, the optimization of mentioned good data structures was performed based on proper layer property, working point, and configuration selection. In general, in the case of property optimization, relatively higher and lower thicknesses were proposed for perovskite layers and transporting layers, respectively. Moreover, lower values were suggested for doping densities of active layers. For the optimization of working points, generally, it was proved that low temperatures and series resistances, and high shunt resistances would be desirable for PSCs. In the configuration selection phase of PSCs, the results represented that single, tandem and composite configuration was proposed for absorber, ETM, and HTM layers, respectively.

Acknowledgements Sakarya University Scientific Research Projects Coordinatorship Unit. is acknowledged by the author for research funding (Project Number: 2022-7-24-118 (LÜTEP)).

Authors’ contribution N. Delibas is the sole contributor to the work.

Funding The research work is supported financially by SRPC (Scientific Research Projects Coordinatorship Unit.), (Project Number: 2022-7-24-118 (LÜTEP)).

Data availability The data and materials used in this study are not published or submitted elsewhere. All the data are available within this report.

Conflict of interest Author declares no conflicts of interests or competing interests that have affected the research work here in the manuscript.

Ethical approval Not applicable (as the present work is non-biological/life science work).

Consent to participate Not applicable (as the present work is non-biological/life science work).

Consent for publication Not applicable (as the present work is non-biological/life science work).

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No competing interests reported.

Improving of a Perovskite Solar Cell Structure by Optimizing Several Simulation Parametric Factors in SCAPS-1D Program

Status:

Version 1

Abstract

Figures

1. Introduction

2. Material and Methods

3. Results and Discussion

3.1. PSC structure selection

3.2. Layer property optimization

3.2.1. Perovskite thickness

4. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1