article · Journal of Science Advanced Materials and Devices
Computer simulations and machine learning models were used to evaluate and compare two designs of Schottky ultraviolet photodetectors: a platinum and zinc oxide structure, and a platinum, few-layer graphene, and zinc oxide structure, both incorporating aluminium contacts. At a bias of minus one volt, the platinum and zinc oxide design achieved an external quantum efficiency of 90.41 per cent, a cut-off frequency of 85.4 gigahertz, and a responsivity of 0.26 amperes per watt. In contrast, the design incorporating few-layer graphene achieved near zero dark current density and a significantly higher 3-dB cut-off frequency of 2.44 terahertz, alongside an external quantum efficiency of 68.52 per cent and responsivity of 0.2 amperes per watt. Machine learning regression models accurately validated the simulation data, establishing a predictive method for refining device design.
Ultraviolet photodetectors are critical components in environmental monitoring, imaging, and high-speed communication systems. Using computational simulations combined with machine learning allows researchers to assess innovative material combinations, such as graphene-enhanced zinc oxide, before fabrication. This approach speeds up the design of ultrafast, highly sensitive optical sensors while significantly reducing the costs and time required for physical prototyping.
The research presents early-stage theoretical and computational findings that could inform the design of high-speed sensors for optical communications, environmental tracking, and ultraviolet imaging systems. Device engineers and optoelectronic hardware developers could use the predictive machine learning framework to optimise future physical prototypes. However, because the study is limited to simulations and computational modelling, significant physical fabrication, testing, and operational validation remain necessary before real-world adoption is possible.
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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/cm 2 , a photocurrent density of 0.26 μA/cm 2 , 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/cm 2 , 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 R 2 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.
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DOI: 10.1016/j.jsamd.2024.100798
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