Machine learning enhanced design and knowledge discovery for multi-junction photonic power converters.

Area of Science:

• Optoelectronics
• Materials Science
• Computational Physics

Background:

• Machine learning (ML) is transforming scientific disciplines, including optoelectronic device design.
• Classical optimization methods can be computationally intensive and may not fully capture complex design spaces.
• Developing efficient photonic power converters requires sophisticated design and optimization strategies.

Purpose of the Study:

• To compare classical versus ML-enhanced methodologies for optoelectronic device design optimization.
• To investigate the application of dimensionality reduction via principal component analysis (PCA) in this context.
• To evaluate the impact on design discovery, optimization efficiency, and understanding of optical phenomena.

Main Methods:

• Simulated the design of ten-junction InP lattice-matched photonic power converters.
• Employed principal component analysis (PCA) for dimensionality reduction of design parameters.
• Compared PCA-enhanced optimization with a classical optimization method.

Main Results:

• PCA-based dimensionality reduction accelerated design discovery and optimization.
• The ML approach yielded over twenty times more optimal designs with greater variability.
• A 15% reduction in computational cost was achieved compared to classical methods.

Conclusions:

• Dimensionality reduction using PCA offers significant advantages in optoelectronic device design.
• This method enhances optimization speed, design diversity, and provides intuitive interpretation of optical phenomena.
• The approach is generalizable, promising knowledge discovery and reduced computational expense in numerical modeling.