Pt/ZnO and Pt/few-layer graphene/ZnO Schottky devices with Al ohmic contacts using Atlas simulation and machine learning

Abstract

The search for highly efficient photodetectors, driven by various applications ranging from environmental monitoring to communication systems and imaging, continues to drive research into novel materials and innovative device architectures. This paper offers an in-depth comparative analysis to optimize the performance of two types of Schottky ultraviolet (UV) photodetectors: platinum (Pt)/zinc oxide (ZnO) and Pt/few-layer graphene (FLG)/ZnO, both featuring aluminum (Al) ohmic contacts. The study systematically examines and compares the electrical and optical characteristics of these two photodetector configurations using the Silvaco TCAD simulation tool and machine learning regression models. The research outcomes demonstrate that the proposed photodetector configurations exhibit remarkable improvements, showcasing their substantial potential for superior performance in UV applications. The Pt/ZnO photodetector demonstrates a dark current density of 8.2 × 10⁻¹¹ pA/cm², a photocurrent density of 0.26 μA/cm², a 3-dB cut-off frequency of 85.4 GHz, an external quantum efficiency of 90.41%, and an external photocurrent responsivity of 0.26A/W, at a bias of −1.0 V. On the other hand, Pt/FLG/ZnO photodetector demonstrates near zero dark current density, a photocurrent density of 0.2 μA/cm², a 3-dB cut-off frequency of 2.44 THz, an external quantum efficiency of 68.52%, and an external photocurrent responsivity of 0.2 A/W at a bias of −1.0 V. Furthermore, a comprehensive comparative analysis of various machine-learning regression models is conducted, validating the simulation findings and providing a predictive framework for optimizing the photodetector's performance. Each machine-learning regression model is evaluated by getting root mean squared error and R² values across different test set sizes to assess their accuracy in predicting the photodetector's characteristics. This study underscores the promising role of cutting-edge materials and computational techniques in advancing the development of next-generation optoelectronic devices with enhanced capabilities and performance.