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Developing High Performance GaP/Si Heterojunction Solar Cells
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Bayesian Optimization-Driven Design of Hydrogen Concentration in A-Si/H Multilayers for Silicon Heterojunction Solar

Soma Kondo1, Yasuyoshi Kurokawa1,2, Kentaro Kutsukake1,3

  • 1Graduate School of Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan.

ACS Applied Materials & Interfaces
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

Optimizing multilayer thin films is complex. This study integrates physical insight with Bayesian optimization, using hydrogen concentration in hydrogenated amorphous silicon (i-a-Si/H) to improve passivation layer quality.

Keywords:
PECVDbayesian optimizationhydrogen concentration controlhydrogenated amorphous siliconmultilayer passivation filmssilicon heterojunction solar cells

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Area of Science:

  • Materials Science
  • Semiconductor Physics
  • Thin Film Technology

Background:

  • Multilayer thin-film passivation structures offer design flexibility but present optimization challenges due to numerous coupled process parameters.
  • Hydrogenated amorphous silicon (i-a-Si/H) passivation layers are crucial for device performance, with hydrogen concentration significantly impacting interfacial passivation and film quality.

Purpose of the Study:

  • To verify an optimization approach combining physical insight with Bayesian optimization for multilayer thin films.
  • To investigate the role of hydrogen concentration in i-a-Si/H passivation layers.
  • To develop a practical framework for optimizing complex thin-film structures.

Main Methods:

  • A three-layer i-a-Si/H passivation structure was used as a model system.
  • Physical insight guided the experimental design, explicitly incorporating hydrogen concentration.
  • Bayesian optimization was employed, with hydrogen flow rate as the control variable for upper layers after initial interfacial layer characterization.

Main Results:

  • A comparative experiment established the impact of varying hydrogen concentration on the interfacial layer.
  • Bayesian optimization successfully refined the upper layer parameters.
  • A graded hydrogen concentration profile was found to be effective for enhancing passivation.

Conclusions:

  • The proposed approach, integrating physical insight with Bayesian optimization, is a practical framework for optimizing multilayer thin films.
  • Explicitly incorporating physically meaningful parameters like hydrogen concentration enhances optimization efficiency.
  • Graded hydrogen concentration profiles are beneficial for achieving high-quality i-a-Si/H passivation layers.