Influence of different-sized inverted-pyramids of silicon texture by Ag manipulation on solar cell performance

Journal Pre-proofs Full Length Article Influence of different-sized inverted-pyramids of silicon texture by Ag manipulation on solar cell performance Juntao Wu, Yaoping Liu, Wei Chen, Yan Zhao, Quansheng Chen, Hanbo Tang, Yan Wang, Xiaolong Du PII: DOI: Reference:

S0169-4332(19)33594-9 https://doi.org/10.1016/j.apsusc.2019.144778 APSUSC 144778

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Applied Surface Science

Received Date: Revised Date: Accepted Date:

14 October 2019 6 November 2019 17 November 2019

Please cite this article as: J. Wu, Y. Liu, W. Chen, Y. Zhao, Q. Chen, H. Tang, Y. Wang, X. Du, Influence of different-sized inverted-pyramids of silicon texture by Ag manipulation on solar cell performance, Applied Surface Science (2019), doi: https://doi.org/10.1016/j.apsusc.2019.144778

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Influence of different-sized inverted-pyramids of silicon texture by Ag manipulation on solar cell performance Juntao Wu2,3, Yaoping Liu1,2*, Wei Chen1,2, Yan Zhao2,3, Quansheng Chen2,3, Hanbo Tang2,3, Yan Wang1,2, and Xiaolong Du1,2,3* 1Songshan

2Key

Lake Materials Laboratory, Dongguan, Guangdong 523808, China

Laboratory for Renewable Energy, Beijing Key Laboratory for New Energy Materials and

Devices, National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China. 3School

of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

E-mail: [email protected], [email protected] ABSTRACT As the development of PV industry, inverted pyramid has become an ideal structure to replace the upright pyramid to boost the cell efficiency. However, there is a serious gap in how surface structures with different-sized inverted pyramids influence the cell performance. In this paper, detailed comparison of optical properties and cell performances among different-sized inverted pyramids and upright pyramid are carried out to give a clear evaluation of how structure influences cell performance. The different-sized inverted pyramids were fabricated with the help of Ag manipulation, which can be attributed to the dramatic increase in the nucleation points prior to Cu deposition due to the existence of Ag during the etching process. Finally, the results indicate a strong connection with the structure size, where the largest size produces the best cell performance. The average cell efficiency of the

inverted pyramids with the largest structure size is 19.955%, which is 0.15% higher than that of the upright pyramids, and the best cell efficiency obtained on the largest inverted pyramids can be as high as 20.094%, indicating that the large inverted pyramids have a great advantage in increasing the cell efficiency. Keywords: Size-Controlled Inverted Pyramids, Ag Manipulation, Nucleation Points, Structure Size, Silicon Solar Cells

1. Introduction In silicon solar cells, mono-crystalline silicon is etched by wet-chemical alkali etching to form an upright pyramid (UP) structure [1-5], and an UP with a smaller structure size has more edges to promote cell performance [6-9]. However, it comes to the bottleneck for the UP to further boost the cell efficiency, for the light reflectance rarely has space to suppress to a lower level [2]. In addition, it is very difficult to find a tradeoff between optical and electrical loss in UPs because the sharp pyramidal peaks degrade the passivation effect during α-Si:H passivation [10-14]. Therefore, inverted pyramids (IPs) have become a good candidate to further elevate cell efficiency due to their free sharp peaks, excellent light-trapping ability and low surface area [15-19]. Indeed, IP-based silicon solar cells have made numerous achievements [20-27]. The highest efficiency was achieved by researchers from the University of New South Wales by designing IPbased PERL/PERT structure cells with an efficiency of 25% [20-21]; whereas, IPs are fabricated with lithography. Many attempts have been made to solve the IP texture problem, but the results have not been satisfactory [28-34]. The large-scale IP texturing problem was not successfully solved until Li Xia Yang et al. adopted the one-step Cu-catalyzed chemical etching (CCCE) method, where micrometer-sized IPs were successfully created with a higher efficiency of 18.87% [35]. However,

some studies have reported that nanometer-sized IPs also have many advantages to improve cell efficiency [36-38]. Therefore, there is a serious gap in the systematic understanding of how surface structures with different-sized IPs influence the cell performance under the same conditions. Hence, the influence of structure size on the cell performance must be clearly evaluated. However, when the CCCE method [35] is applied to texture diamond-wire-saw (DWS) silicon wafers, a texturing problem arises, resulting in a textured surface covered by incomplete IPs with saw grooves, which will decrease the cell performance. Noticeably, the CCCE method [35] was established on slurry-wire-saw (SWS) wafers, where the mass of surface defects that are a prerequisite for the texture process is distributed homogeneously [39-40]. However, DWS technology, which has fully replaced the SWS method, results in a huge change in IP texturization [41-44] with fewer damaged layers and saw grooves along with the lower redox potential of Cu2+/Cu species [45], which strongly hinder Cu deposition due to the lack of nucleation points. Therefore, a novel method is needed to solve this problem. Encouragingly, the higher redox potential of Ag+/Ag species allows the possibility of solving the texturing problem, and this method was utilized in our previous work to efficiently texture DWS mcSi wafers with a superhigh cell efficiency [46]; Cu/Ag-cocatalyzed chemical etching not only eliminates the saw grooves but also obscures the grain boundaries. However, the final structure is nanometer size, which is sluggish in increasing the cell efficiency because of the enlarged specific surface area. Thus, a balance between the structure size and the saw grooves must be reached. In this paper, the balance between the structure size and saw grooves was achieved by Ag manipulation, resulting in thoroughly texturing DWS wafers with size-controlled inverted pyramids. Moreover, the detailed comparison of the reflectance and cell performance was executed for samples

with different-sized IPs and UPs, filling the knowledge gap mentioned above. The results indicate a strong connection with the structure size, where the largest size results in the best performance. Thanks to the excellent light trapping ability of the IPs, the average cell efficiency of the IPs with the largest structure size is 19.955%, which is 1.5% absolute higher than the UP. Besides, the best cell efficiency of IPs can be as high as 20.094% with a much higher Jsc of 41 mA/cm2, indicating that the IPs with larger size has great advantage in increasing cell efficiency.

2. Material and Methods The materials used in this study are boron-doped mono-like-crystalline Si wafers cut by the DWS technique with a commercial size of 156 mm × 156 mm. The wafers are textured by CCCE and labeled sample A, sample B and sample C based on the different sizes of the IP structures. The inverted pyramid samples are carried out in a polytetrafluoroethene tank filled with 20 mM Cu(NO3)2, 4.6 M HF, 0.55 M H2O2 and specific AgNO3 concentrations (0.5 mM for sample A, 0.06 mM for sample B, and 0.002 mM for sample C) for 5 minutes at room temperature; some details about Cu deposition have been discussed in our previous work [35, 45-48]. As a reference, the upright pyramid texture is obtained by etching in alkaline solutions containing 2 wt% potassium hydroxide (KOH) and 10 vol% IPA for 20 minutes at 75°C [48], which is labeled sample R. Before being fabricated into solar cells, the samples are treated with standard RCA cleaning and KOH post-treatment to obtain a smooth surface. The KOH post-treatment was carried out on the production line with 5 wt% KOH at room temperature for 30 s. The morphologies and structures of the samples are characterized by a Zeiss Sigma-300 scanning electron microscope. Hemispheric total reflectance for normal incidence is measured using a Varian Cary 5000 spectrophotometer equipped with an integrating sphere. The effective lifetime τ eff is

measured by WT-2000 in Quasi-Steady State Photoconductance (QSSPC) mode. The solar cell efficiency is measured using a BERGER Lichttechnik Single Cell Tester. The quantum efficiency (QE) is measured by the BENTHAM PVE300-IVT system.

3. Results and Discussion 3.1 The fabrication of samples with different-sized inverted pyramids Fig. 1 displays the SEM images of the IP texturization through Cu-catalyzed chemical etching on DWS c-Si with different concentrations of Ag. As we can see from Fig. 1(a), the IPs textured by only the Cu etching system on DWS wafers are not complete and have rougher side walls. In addition, it is obvious that the deviation in the structure sizes is large; a large number of the saw grooves exist on the textured surfaces, which are covered by parasitic burrs (marked with red circles in Fig. 1(a)), and these burrs are difficult to effectively passivate. In Fig. 1(b), the textured surface with 0.5 mM Ag is flat, and no saw grooves or parasitic burrs are found. However, the achieved structure is nanometer in size. Therefore, a tradeoff between the structure size and the saw grooves must be reached, and reincorporating Ag via adjusting the Ag concentration is a good choice. Indeed, by changing the Ag concentration, different-sized IPs are obtained, and the results are shown in Fig. 1(c) and (d). The structure size of the IPs increases as the Ag concentration decreases. Surprisingly, uniform micrometer-sized IPs without burrs and saw grooves attached are successfully fabricated with 0.002 mM Ag (Fig. 1(d)). Thus, reincorporating Ag by using various Ag concentrations is an efficient method to settle the texturing problem caused by the Cu-etching system, leading to the texture of the DWS c-Si with uniform different-sized IPs from nanometer to micrometer size, and the smaller the Ag concentration is, the larger the structure size. The detailed SEM images are shown in Supporting Information, Fig. S1.

The mechanism of Ag in decreasing the DWS grooves and regulating the IP size is systematically studied, and the scheme images are listed in Fig. 2. As mentioned above, the DWS technique does not work for Cu deposition due to the lack of nucleation points (Fig. S2(a)), resulting in the deposited Cu gathering in the saw grooves, which still exist as the reaction continues (Fig. S3). The reincorporated Ag provides a solution and makes the Cu deposition homogeneous (Fig. S2(b), (c)) due to the higher redox potential of Ag+/Ag species. The initial stages of texturization are greatly changed with Ag reincorporation and vary among the different Ag concentrations, which can be observed in Fig. 2(a), (b), (c). The nucleation points dramatically increase upon reincorporating Ag in the first 30 s of texturization; the surface textured by 0.5 mM has the largest number of nucleation points (Fig. 2(c)), and 0.002 mM has the least (Fig. 2(a)). The higher redox potential of Ag+/Ag upon the Si valence band get the Ag deposition rid of the influence from the wafer surface, which determines that the behavior of Ag deposition is isotropic and more easily take places than Cu deposition. Thus, Ag deposition occurs on the saw grooves and other surface areas, leading to the generation of nucleation points on the grooves. Additionally, the number of nucleation points is positively related to the Ag concentration; the higher the Ag concentration, the larger the number of nucleation points. The increase nucleation points successfully cut off the saw grooves and rough the wafer surface, which allows uniform structure formation and the disappearance of the DWS saw grooves. Concurrently, the reincorporated Ag increases the chance for Cu deposition because the increased nucleation points contribute to uniform Cu deposition. In return, the uniformly deposited Cu permits more nucleation points to grow into integrated IPs, and no saw grooves exist (Fig. 1 and Fig. 2). On the other hand, a large number of nucleation points results in obvious restrictions on the merging of deposited Cu, which intensively localize the Cu etching process [46] and isolate the deposited Cu from the adjacent Cu, allowing more

nucleation points to grow into IPs at the expense of further expanding the structure size (Fig. 2(d), (e), (f)). Due to the restriction of Cu merging and hence the localized Cu etching, the various IPs sizes are engendered as the etching process proceeds, resulting in the formation of different size IPs, which are illustrated in Fig. 2(h), (j), and (k). To further elaborate the role of Ag in the Cu chemical etching system to form different-sized IPs, schematics of the different stages with different Ag concentrations are shown in Fig. 3. Due to the higher redox potential of Ag+/Ag, Ag nanoparticles (NPs) will first deposit on the Si surface prior to Cu NPs deposition in the initial stage of texturing (Fig. 3(a), (b)) due to the lower redox potential of Cu2+/Cu species. The deposited Ag NPs, which prefer vertical etching [46], promote the etching process, resulting in nanopits covering the surfaces. Additionally, as the Ag concentration increases, Ag NP deposition becomes faster, leading to more Ag NPs covering the surface in the initial stage; the increase in Ag accelerates the initial etching process. As a consequence, nanoholes are formed as the Ag concentration increases with the same etching time (Fig. 2(c), Fig. 3(b)). The variance in the initial stage serves a crucial background for the final structure, and the resultant texturing provides strong proof. When the amount of Ag+ in the solution is less than that of Cu2+, e.g., three or more fold lower, the Ag NP deposition does not dominate a prolonged texturing process. Furthermore, the Cu NPs being depositing soon after Ag NP deposition, and the nanopits formed in the initial stage contribute to Cu deposition. Thus, the expedited Cu deposition together with the much larger amount of Cu2+ causes the Cu2+/Cu species to dominate the remaining IP etching (Fig. 3(c), (d)), which has been detailed in our previous work [35, 45-48]. However, it is worth mentioning that the Cu deposition is greatly localized and isolated because of the strong vertical-etching ability of Ag, which places a forceful restriction on Cu NP merging, and the higher the Ag concentration is, the stronger the restriction. The

delay in the Cu NP merging hinders further expansion of the structure size. Consequently, the size of the IPs is strongly associated with the Ag concentration, where the lower the Ag concentration is, the larger the structure size (Fig. 3(e), (f)), which is also shown in Fig. 2. When the amount of Ag+ in solution is equivalent to that of Cu2+, the vertical-etching ability of Ag will not merely restrict Cu NP merging but will also orient the etching process, resulting in a much smaller sized structure, as shown in Fig. 2(k) and in reference [46]. Therefore, by reincorporating Ag and adjusting the Ag concentration, the saw grooves disappear and size-controlled IPs are acquired. As mentioned in the introduction part, there are some studies reporting that the nanometer-sized IPs also have advantages to improve the cell efficiency; however, a serious lack of IP comparison among different structure sizes prevents us from giving a clear evaluation. Therefore, in this work, a detailed comparison of the optical properties and cell performances is given for IPs with different structure sizes, and the samples are labeled sample A for the small size, sample B for the middle size, and sample C for the large size. The upright pyramid is also compared as a reference, which is labeled sample R, and their corresponding SEM images are shown in Fig. 4(a), (b), (c) and (d), respectively. In addition, additional SEM images for these different samples are shown in Fig. S4. The statistical results for the size distribution carried out with the help of e-ruler were shown in Fig. 4. It is obvious that the structure size is different among the samples, and the structure size of sample A, sample B, sample C and sample R is approximately 550 nm, 880 nm, 2600 nm and 1400 nm, respectively. Due to reincorporated Ag, the saw grooves for all the IP samples are efficiently eliminated. To properly evaluate the cell performance, the IP samples should be smoothed with a KOH post-treatment step before being fabricated into solar cells because the metal deposition is relatively random, even the sidewalls can be etched, giving rise to a rough surface (Fig. 2(h), (j), (k)), and KOH post-treatment is

an effective method to modify the surface and has been widely used in production lines. The reflectance spectra over the wavelength range from 300 nm to 1100 nm for these samples before SiNx deposition are illustrated in Fig. 5(a). In addition, the reflectance values of different samples versus θ ranging from 0° to 60°, where θ is the angle of incidence (AOI), are also displayed in Fig. 5(b). It is obvious that the reflectance of the inverted samples is suppressed to a lower level well below that of the pyramid samples, regardless of the AOI, due to the excellent light trapping ability of IPs. Nonetheless, for the inverted samples, the reflectance increases obviously as the structure size of the IPs decreases; the reason for which is that the surface is not completely covered with IPs when the structure size decreases to a point where more flat areas exist, as shown in Fig. 1 and Fig. 4, resulting in the reflectance increases. As a result, a larger structure size results in fewer flat areas and lower reflectance. Thus, the reflectance of sample C with the largest size of IPs is the lowest at 6.5%, while that of sample A is the highest at 10.0%. The optical properties of the different samples displayed above strongly indicate that the IP samples, especially sample C of the IP with the largest structure size, can greatly increase the cell efficiency. Given that the difference in the structure size strongly influences the reflectance, the structure sizes will affect the cell performance similarly because the reflectance has a close relationship with JSC, which can be described as follows [49]: 𝜆

[1 ― 𝑅(𝜆)] ∗ 𝐼𝐴𝑀1.5(𝜆)

𝐽𝑠𝑐 = ∫𝜆𝑚𝑎𝑥 𝑚𝑖𝑛

ℎ𝑐/(𝑞𝜆)

𝑑𝜆

(1)

Where 𝝀 is the wavelength of incident light, h is the Planck constant, c is the velocity of light, q is the quantity of electric charge, and 𝑹(𝝀) is reflectance of the specific incident light 𝝀, 𝑰𝑨𝑴𝟏.𝟓(𝝀) is the standard solar spectrum under the AM1.5 condition. Therefore, the Isc can be expressed by:

[1 ― 𝑅(𝜆)] ∗ 𝐼𝐴𝑀1.5(𝜆)

𝜆

𝐼𝑆𝐶 = 𝐴 ∗ 𝐽𝑆𝐶 = 𝐴 ∗ ∫𝜆𝑚𝑎𝑥

ℎ𝑐/(𝑞𝜆)

𝑚𝑖𝑛

(2)

𝑑𝜆

Where A is the area of the silicon wafer. As we know, Uoc has a relation with 𝑰𝒍, which is shown as follows [49]:

(

𝐾𝑇

𝑈oc =

𝑞 𝑙𝑛

)

𝐼𝑙

(3)

+1

𝐼0

Where 𝑰𝒍 is the photo-generated current, 𝑰𝟎 is the reverse saturation current. On the other hand, Isc also can be expressed by the following equation:

[

𝑞(𝑉 + 𝐼𝑅𝑆)

𝐼𝑆𝐶 = 𝐼𝐿 ― 𝐼0 𝑒

𝐾𝑇

]

―1 ―

(𝑉 + 𝐼𝑅𝑆)

(4)

𝑅𝑠ℎ

Where V is the voltage on the load, which is zero when the load is in a state of short circuit; Rs is the series resistance, Rsh is the shunt resistance. Actually, when the cell works well, 𝑅𝑆→0, 𝑅𝑠ℎ→∞, thus, (5)

𝐼𝑆𝐶 = 𝐼𝑙 Therefore, 𝑈oc =

𝐾𝑇

𝑞 𝑙𝑛

Where 𝑈oc = 𝜆

𝑞

𝐼0

𝑙𝑛

𝐼𝑆𝐶 𝐼0

)

(6)

+1

≫ 1 because of 𝐼𝑆𝐶 ≫ 𝐼0, so Uoc can be further expressed by the following formula:

( )= 𝐼𝑆𝐶

𝐾𝑇

𝐼0

𝑞

(ln 𝐼𝑆𝐶 ― ln 𝐼0) =

[1 ― 𝑅(𝜆)] ∗ 𝐼𝐴𝑀1.5(𝜆)

ln ∫𝜆𝑚𝑎𝑥 𝑚𝑖𝑛

𝐾𝑇

𝐼𝑆𝐶

(

ℎ𝑐 𝑞𝜆

𝑑𝜆 +

𝐾𝑇

𝑞 ln 𝐴 ―

𝐾𝑇 𝑞

[1 ― 𝑅(𝜆)] ∗ 𝐼𝐴𝑀1.5(𝜆)

𝜆

ln 𝐴 ∗ ∫𝜆𝑚𝑎𝑥

𝐾𝑇

𝑞 ln 𝐼0 =

ℎ𝑐 𝑞𝜆

𝑚𝑖𝑛

𝐾𝑇

𝜆

[1 ― 𝑅(𝜆)] ∗ 𝐼𝐴𝑀1.5(𝜆)

𝑚𝑎𝑥 𝑞 ln∫𝜆 𝑚𝑖𝑛

𝑑𝜆 ―

ℎ𝑐 𝑞𝜆

𝐾𝑇 𝑞

ln 𝐼0 =

𝑑𝜆 +

𝐾𝑇 𝑞

𝐾𝑇 𝑞

𝐴

ln 𝐼0 (7)

Therefore, we can extract a relationship between Uoc and R(𝝀) from equations (7): 𝑈oc ∝ 𝐼𝑆𝐶 ∝ [ ―𝑅(𝜆)],

(8)

This relationship indicates that the suppression of the reflection 𝑹(𝝀) not only results in increasing Isc but also promotes Uoc. Regarding the superiority of IPs for trapping light, a much lower reflection can greatly improve cell efficiency. Therefore, sample C would greatly improve cell efficiency. 3.2 The cell performances for different samples

To verify the distinctions in structure sizes for cell performance, mono-like-crystalline Si wafers for different samples were fabricated into solar cells with the current Al-BSF production line suited for mc-Si. The sample quantities of sample A, sample B, sample C and sample R are 200, 200, 600 and 300 slices, respectively. Fig. 6(a) shows the reflectance spectra of different samples after SiNx deposition, showing the same tendency as the samples without the AR-coating, where the reflectance of IP samples is lower than the UP one, owing to the excellent light-trapping ability of the IP. The reflectance after SiNx is 1.9% for sample A, 1.4% for sample B, 1.1% for sample C and 3.1% for sample R, respectively. Besides, to clearly reveal the influence of the surface structures on the surface recombination, the effective lifetime τeff is measured for different samples after texturing and SiNx deposition, whose results are listed in Fig. 6(b). We can find that theτeff was increasing as the structure size increases after texturization and SiNx deposition for the inverted pyramid samples, which can be attributed to the greatly suppress of the surface defects due the decrease of the specific surface area. Additionally, it is should be mentioned that theτeff of sample C was lower than that of the upright pyramid after SiNx deposition even though the sample C has a higherτeff after texturization, indicating that further optimization is needed to be done to improve the passivation effect of inverted pyramid. The best and average cell parameters measured by a BERGER Lichttechnik Single Cell Tester in the production line for different samples are listed in Fig. 7. The box plot shows the main electric performance parameters, including the conversion efficiency (Eta), short-circuit current density (Jsc), fill factor (FF) and open-circuit voltage (Uoc). It is obvious that sample C has the best cell performance, with an average cell efficiency as high as 19.955%, a Uoc of 638.6 mV and a Jsc of 38.58 mA/cm2, which is 0.15% higher than that of sample R and indicates the powerful improvement in the cell

efficiency. For the inverted samples, the average cell parameters have a close correspondence with the structure size; i.e., a larger size results in better cell performance, as shown in Fig. 7. Sample C has the best cell performance, sample A the worst, and sample B is in-between the two. In comparison with the upright pyramid sample (sample R), a slight deviation arises, where sample A and sample B have poorer performance than sample R, even though the two IP samples (sample A and sample B) have a lower reflectance (<10%) than the pyramid (>10%). This phenomenon can be explained by the poor passivation effect of these two IP samples whose structure size is submicrometer, leading to a larger specific surface area, where the surface defects decrease the cell performance. The Uoc of the two samples confirms the above explanation, especially for sample B with a higher Jsc of 38.35 mA/cm2 but a much lower Uoc of 626.5 mV than sample R, 638.6 mV with Jsc of 38.25 mA/cm2. However, sample C with the largest IP size results in an inversion. The largest structure size greatly reduces the specific surface area, hence elevating the passivation effect to promote the average Uoc to 638.6 mV, which is almost the same as the UP value. In total, the highest Isc together with a larger Uoc and FF make sample C the best among these samples, which again proves that IPs have a significant advantage in improving the Jsc to further boost the cell efficiency. Photographs of solar cells with sample C and sample R are shown in the inserts of Fig. 8(a). The cell appearance of sample C is dark blue, while sample R is blue, and this result agrees with the reflectance mentioned above. Moreover, we measured the external quantum efficiency (EQE) spectra to gain insight into the correlation between the Jsc and optical harvesting, and the spectra are shown in Fig. 8(a); Fig. 8(b) shows the corresponding EQE enhancement factor, which is the ratio of the EQE for these four samples to the EQE for the sample with UP (EQESample A, B C or R / EQESample R). The EQE result coincides well with the cell efficiency results; i.e., the best cell efficiency has the best EQE

performance over the whole wavelength spectra from 300 nm to 1100 nm. In addition, there is an obvious enhancement in the short wavelength range from 300 nm to 600 nm (Fig. 8(a)) as the structure size increases to be larger than the sample R, meaning that the structure with a larger size is benefit to collect the generated carriers near the surface, leading to a better short wavelength resonance. In the long wavelength (1000nm -1100nm), the sample B and sample C display nearly the same performance, where the EQE of sample C is just slightly higher than sample B. However, both have a better performance than sample R. As for sample A with the smallest structure size, effective passivation is difficult to achieve, resulting in the worst performance. The EQE results show that the surface with large sized structure is good at promoting the cell performance. In addition, the IQE (internal quantum efficiency) is extracted from the EQE and reflectance (IQE=EQE/1-R, where R is the reflectance) for these samples to further exclude the carrier collection efficiency, which is seen in Fig. 9(a); additionally, the partially enlarged detail of the IQE in the long wavelength region is also listed in Fig. 9(b). IQE is a direct method to reflect carrier recombination. In this study, sample R as a reference shows the best performance at wavelengths from 300 nm to 900 nm; correspondingly, the IP samples behave worse, meaning that the front surface recombination for the IP samples is much heavier than that for the pyramid samples, which has a good agreement with the results of τ eff. The reason for this is that there are still some surface defects on the IPs, such as the rougher surface generated by MCCE and the poor passivation effect. However, in the long wavelength region from 900 nm to 1100 nm (Fig. 9(b)), the IP samples, particularly sample C, perform better than sample R, meaning that the rear surface recombination of the IPs is better suppressed, which possibly can be attributed to the fact that the IPs can more easily obtain a flat rear surface than the upright pyramids during the back surface polishing process because of the more even distribution of

the structure size, which is essential for the cell efficiency enhancement. Additionally, among all the IPs samples, sample C with the largest IP structure size has the best IQE performance over the whole wavelength, meaning that the interface defects have been efficiently restricted, which can be attributed to the surface area and parasitic burrs decreasing as the structure size increases with the help of reincorporated Ag. Until now, regardless of the cell parameters or EQE/IQE performance, the sample surface with the largest structure size (sample C) have more space to further boost the cell efficiency because of the enhanced light-trapping ability and improved passivation effect, and the best cell efficiency for sample C can be as high as 20.094%. Finally, it is worth pointing out that the cell fabrication conditions in the present work are not optimized; thus, the cell parameters, particularly Uoc and FF, can be further improved by adopting high-quality passivation methods, e.g., stack passivation.

4. Conclusions In summary, the different-sized inverted pyramids from nanometer to micrometer scale were fabricated through reincorporating Ag as an efficient catalyst in Cu etching system. The role of Ag in different-sized inverted-pyramid texturing is that Ag can dramatically increase the nucleation points prior to Cu deposition; thus, a higher Ag concentration results in more nucleation points and a smaller structure size. Moreover, the detailed comparisons from optical properties to cell performances clearly reveals that the surface with the largest structure size has the greatest advantage in increasing the cell efficiency. Thus, the average cell efficiency of the IPs with the largest structure size is as high as 19.955% with Uoc of 638.6 mV and Jsc of 38.58 mA/cm2, which is 1.5% higher than that of the UP. In addition, the best cell efficiency obtained with the largest-sized inverted pyramid sample exceeded 20% and reached 20.094%, further confirming that IPs with larger sizes have great advantages in

increasing cell efficiency.

Notes There are no conflicts to declare.

Acknowledgements This work is supported by the National Science Foundation of China (grant nos. 11675280, 61874139, and 11674405) and the Science and Technology Department of Jiangsu Province (Technological Achievements Transformation Project, grant no. BA2017137).

Supporting Information Partial enlarged view SEM images for Cu-catalyzed chemical etching on DWS c-Si with or without Ag; SEM images of the behavior of Cu deposition with or without Ag; SEM images of different stages for Cu-catalyzed chemical etching on DWS c-Si without Ag; SEM images for different samples; cell photographs for different samples; views of mono-like-crystalline silicon wafer quality tests (PDF)

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Figures

Fig. 1. SEM images for Cu-catalyzed chemical etching on DWS c-Si with different concentrations of Ag : (a) with no Ag; (b) with 0.5 mM Ag obtaining small-sized IPs; (c) with 0.066 mM Ag obtaining mid-sized IPs; (d) with 0.002 mM Ag obtaining large-sized IPs, where the saw grooves disappear compared with (a) due to the incorporated Ag.

Fig. 2. SEM images of different stages of Cu-catalyzed chemical etching on DWS c-Si with different Ag concentrations: (a) 30 s, (d) 90 s, and (h) 210 s for the 0.002 mM Ag; (b) 30 s, (e) 90 s, and (j) 210 s for 0.066 mM Ag; (c) 30 s, (f) 90 s, and (k) 210 s for 0.5 mM Ag. The higher the Ag concentration is, the greater the initial texturing point and the smaller the structure size.

Fig. 3. The schematics of different Ag concentrations in the Cu texturing system with size-controlled IPs: (a), (c), (e) low Ag concentration, e.g. 0.002 mM Ag; (b), (d), (f) high Ag concentration, e.g. 0.066 mM Ag. (a), (b) the initial stage of texturing; (c), (d) the middle stage of texturing; (e), (f) the final stage of texturing.

Fig. 4. SEM images and structure-size statistical results for different samples: (a), (f) sample A; (b), (h) sample B; (c), (j) sample C; and (d), (k) sample R. All the SEM images of inverted pyramids for different samples shown here are captured after KOH post-treatment. Sample R is the upright pyramid as reference.

Fig. 5. (a) Reflectance spectra for the wavelength range from 300 nm to 1100 nm for the samples before SiNx deposition; (b) reflectance of different samples versus θ, where θ is the angle of incidence (AOI).

Fig. 6. Reflectance spectra and effective lifetimeτeff for different samples: (a) reflectance ranging from 300nm to 1100nm after SiNx deposition; (b) effective lifetimeτeff after texturing and SiNx deposition.

Fig. 7. Box plot of cell parameters for different samples: (a) Eta, (b) Jsc, (c) FF and (d) Uoc. The box plot shows the lower quartile, median, and upper quartile and the symbols □, △, and ▽ represent the mean, maximum, and minimum of the data, respectively.

Fig. 8. EQE spectra (a) and EQE enhancement (b) from 300 nm to 1100 nm for different samples. The inserts in (a) show cell photographs of the different samples.

Fig. 9. IQE spectra: (a) the whole wavelength range from 300 nm to 1100 nm; (b) partially enlarged detail of IQE in the long wavelength range from 800 nm to 1100 nm.

Highlights

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Size-controlled inverted-pyramids from nanometer to micrometer scale were fabricated with Ag manipulation.

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The mechanism of Ag manipulation in IPs size is Ag can dramatically increase the nucleation points prior to the Cu deposition.

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the detailed comparisons among different-sized inverted-pyramids and upright pyramid were carried out to give a clear evaluation about how structure influences cell performance.

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The average cell efficiency of IPs with the largest structure size is 19.955%, which is 0.15% absolute higher than the one with upright pyramid.

Authors contribution statement: Juntao Wu: Writing - Original Draft, Conceptualization, Investigation; Yaoping Liu: Methodology, Validation, Visualization; Wei Chen: Formal analysis, Methodology; Yan Zhao: Resources, Formal analysis; Quansheng Chen: Software; Hanbo Tang: Data Curation; Yan Wang: Validation, Visualization; Xiaolong Du: Supervision, Project administration