Photoresponsivity enhancement of SnS-based photosensors using Machine Learning and SCAPS simulations.

Tin Sulfide (SnS)-based photodetector and photovoltaic devices emerged as potential candidates for low-cost and efficient eco-friendly photosensing and clean energy applications. The amazing optoelectronic properties of SnS-based devices, such as high optical absorption and tunable direct band-gap, are currently piquing the curiosity of researchers. However, the low recorded photoresponsivity is the major limitation that needs to be overcome without introducing toxic materials and increasing the elaboration cost of the solar cell. 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 photosensing photovoltaic applications. It is revealed that the proposed design framework can predict the better SnS photovoltaic configuration, and 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 heterostructure solar cell offers an innovative approach for elaboration of eco-friendly high-efficiency thin-film optoelectronics devices that is more promising than the previously reported designing techniques.