Photoresponsivity Enhancement of SnS-Based Devices Using Machine Learning and SCAPS Simulations†

Abstract

In this work, we propose a novel alternative design technique based on combined SCAPS numerical simulations and Machine Learning (ML) computation to improve the photocurrent performances for efficient eco-friendly optoelectronic applications. In this context, a new SnS absorber structure based on introducing gold (Au) nanoparticles (NPs) is proposed. It is revealed that the proposed design framework can predict the best spatial distribution of Au NPs, allowing for the enhanced optical behavior of SnS absorber film. This can pave the way for the optoelectronic systems designers to identify the geometry and the appropriate material for each layer of the device. Moreover, the results of the proposed SnS-based structure offer an innovative approach for the elaboration of eco-friendly, high-efficiency thin-film optoelectronics devices that is more promising than the previously reported designing techniques.