Experiment and simulation in silicon PN-junction photodetectors: insights into electrical and optical transport

Abstract

Silicon PN junctions remain central to optoelectronic technologies due to their maturity and CMOS compatibility. We report the fabrication and comprehensive optoelectronic characterization of a silicon PN-junction photodiode demonstrating stable operation over a wide temperature range. The device exhibits excellent diode behavior, with a rectification ratio exceeding four orders of magnitude, an ideality factor close to unity above 0.3 V, and a series resistance below 100 Ω. Under white-light illumination, the photodiode shows a linear photocurrent response over broad optical power and temperature ranges, achieving an average responsivity of 0.3 A W−1. We implement a machine learning framework based on an Artificial Neural Network to perform global parameter estimation, demonstrating its effectiveness in generalizing across diverse experimental datasets. Moreover, we propose a comprehensive analytical model, validated by Atlas–Silvaco simulations, that successfully captures charge transport and photogeneration mechanisms. This integrated approach, combining experimental measurements, machine learning, numerical simulations and analytical modelling, provides a robust performance benchmark and deeper insights for optimizing silicon-based optoelectronic devices.