Application of Bayesian optimization for improved passivation performance in TiO<sub>
                  x
               </sub>/SiO<sub>
                  y
               </sub>/c-Si heterostructure by hydrogen plasma treatment

Miyagawa, Shinsuke; Gotoh, Kazuhiro; Kutsukake, Kentaro; Kurokawa, Yasuyoshi; Usami, Noritaka

doi:10.35848/1882-0786/abd869

Cited by 19 publications

(8 citation statements)

References 33 publications

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“…Miyagawa et al [134] optimized the process conditions of TiO x /SiO y /c-Si heterostructures in hydrogen plasma treatment (HPT) using the BO method to improve their surface interface passivation properties. The researchers first deposited 3 nm TiOx films on c-Si substrates, and some of the samples were oxidized by H 2 O 2 to form a SiO y interlayer before TiO x deposition.…”

Section: Process Optimizationmentioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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Optoelectronic materials are essential for today's scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

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Section: Process Optimizationmentioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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“…Recently, Bayesian optimization (BO), a machine learning method for sequential optimization, has been widely used to find optimum experimental conditions. − The BO algorithm provides the next experimental condition based on the probabilistic model for the regression of the data. By alternately repeating the experiments and determining the next experimental condition using the BO algorithm, the appropriate condition for maximizing (or minimizing) the object function can be determined with a small number of experiments.…”

Section: Introductionmentioning

confidence: 99%

High Passivation Performance of Cat-CVD i-a-Si:H Derived from Bayesian Optimization with Practical Constraints

Ohashi,

Kutsukake,

Thi Cam Tu

et al. 2024

ACS Appl. Mater. Interfaces

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High-quality passivation with intrinsic hydrogenated amorphous Si (i-a-Si:H) is essential for achieving highefficiency Si heterojunction (SHJ) solar cells. The formation of i-a-Si:H with a high passivation quality requires strict control of the hydrogen content and film density. In this study, we report the effective discovery of i-a-Si:H deposition conditions through catalytic chemical vapor deposition using Bayesian optimization (BO) to maximize the passivation performance. Another contribution of this study to materials science is the establishment of a practical BO scheme consisting of several prediction models in order to account for the practical constraints. By applying the BO scheme, effective minority carrier lifetime (τ eff ) is maximized within the deposition condition range, while being constrained by the i-a-Si:H thickness and the capabilities of the experimental setup. We achieved a high passivation performance of τ eff > 2.6 ms with only 8 cycles in BO, starting with 14 initial samples. Within the investigated range, the deposition conditions were further explored over 20 cycles. The BO provided not only optimal deposition conditions but also scientific knowledge. Contour plots of the predicted τ eff values obtained through the BO process demonstrated that there is a band-like high τ eff condition in the parameter space between the substrate temperature and SiH 4 flow rate. The high void fraction and epitaxial growth were inhibited by controlling the substrate temperature and SiH 4 flow rate, resulting in a high passivation quality. This indicates that the combination of the SiH 4 flow rate and substrate temperature parameters is crucial to passivation quality. These results can be applied to determine the deposition conditions for a good a-Si:H layer without a high void fraction or epitaxial growth. The research methods shown in this study, practical BO scheme, and further analysis based on the optimized results will be also useful to optimize and analyze the process conditions of semiconductor processes including plasmaenhanced chemical vapor deposition for SHJ solar cells.

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“…BO is a powerful method to optimize multiple parameters based on probabilistic predictions, leading to the reduction of the number of experiments for optimization in a multidimensional parameter space [34]. For example, Miyagawa et al recently applied BO to the optimization of seven parameters including HPT process parameters in TiOx/SiOy/c-Si structures and reported the achievement of a significant improvement in carrier selectivity in just 12 experiment cycles [35]. Therefore, in this study, we attempted to optimize the HPT process parameters efficiently by applying BO to prepare low defect Si-QDML process.…”

Section: Introductionmentioning

confidence: 99%

Bayesian optimization of hydrogen plasma treatment in silicon quantum dot multilayer and application to solar cells

et al. 2023

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Silicon quantum dot multilayer (Si-QDML) is a promising material for a light absorber of all silicon tandem solar cells due to tunable bandgap energy in a wide range depending on the silicon quantum dot (Si-QD) size, which is possible to overcome the Shockley–Queisser limit. Since solar cell performance is degenerated by carrier recombination through dangling bonds (DBs) in Si-QDML, hydrogen termination of DBs is crucial. Hydrogen plasma treatment (HPT) is one of the methods to introduce hydrogen into Si-QDML. However, HPT has a large number of process parameters. In this study, we employed Bayesian optimization (BO) for the efficient survey of HPT process parameters. Photosensitivity (PS) was adopted as the indicator to be maximized in BO. PS (σp/σd) was calculated as the ratio of photoconductivity (σp) and dark conductivity (σd) of Si-QDML, which allowed the evaluation of important electrical characteristics in solar cells easily without fabricating process-intensive devices. 40-period layers for Si-QDML were prepared by plasma-enhanced chemical vapor deposition method and post-annealing onto quartz substrates. Ten samples were prepared by HPT under random conditions as initial data for BO. By repeating calculations and experiments, the PS was successfully improved from 22.7 to 347.2 with a small number of experiments. In addition, Si-QD solar cells were fabricated with optimized HPT process parameters; open-circuit voltage (VOC) and fill factor (FF) values of 689 mV and 0.67, respectively, were achieved. These values are the highest for this type of device, which were achieved through an unprecedented attempt to combine HPT and BO. These results prove that BO is effective in accelerating the optimization of practical process parameters in a multidimensional parameter space, even for novel indicators such as PS.

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Application of Bayesian optimization for improved passivation performance in TiO_x/SiO_y/c-Si heterostructure by hydrogen plasma treatment

Cited by 19 publications

References 33 publications

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

High Passivation Performance of Cat-CVD i-a-Si:H Derived from Bayesian Optimization with Practical Constraints

Bayesian optimization of hydrogen plasma treatment in silicon quantum dot multilayer and application to solar cells

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Application of Bayesian optimization for improved passivation performance in TiO x /SiO y /c-Si heterostructure by hydrogen plasma treatment

Cited by 19 publications

References 33 publications

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

High Passivation Performance of Cat-CVD i-a-Si:H Derived from Bayesian Optimization with Practical Constraints

Bayesian optimization of hydrogen plasma treatment in silicon quantum dot multilayer and application to solar cells

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Application of Bayesian optimization for improved passivation performance in TiO_x/SiO_y/c-Si heterostructure by hydrogen plasma treatment