Optoelectronic performance prediction of HgCdTe homojunction photodetector in long wave infrared spectral region using traditional simulations and machine learning models

Abstract This research explores the design of an infrared (IR) photodetector using mercury cadmium telluride (Hg 1– x Cd x Te). It proposes two- and three-dimensional homojunction models based on p + -Hg 0.7783 Cd 0.2217 Te/n – -Hg 0.7783 Cd 0.2217 Te, focusing on applications in the long-wavelength infrared range. The photodetector’s performance is analyzed using Silvaco ATLAS TCAD software and compared with analytical calculations based on drift-diffusion, tunneling, and Chu’s approximation techniques. Optimized for operation at 10.6 μm wavelength under liquid nitrogen temperature, the proposed photodetector demonstrates promising optoelectronic characteristics including the dark current density of 0.20 mA/cm 2 , photocurrent density of 4.98 A/cm 2 , and photocurrent density-to-dark current density ratio of 2.46 × 10 4 , a 3-dB cut-off frequency of 104 GHz, a rise time of 0.8 ps, quantum efficiency of 58.30 %, peak photocurrent responsivity of 4.98 A/W, specific detectivity of 3.96 × 10 11 cmHz 1/2 /W, and noise equivalent power of 2.52 × 10 –16 W/Hz 1/2 indicating its potential for low-noise, high-frequency and fast-switching applications. The study also incorporates machine learning regression models to validate simulation results and provide a predictive framework for performance optimization, evaluating these models using various statistical metrics. This comprehensive approach demonstrates the synergy between advanced materials science and computational techniques in developing next-generation optoelectronic devices. By combining theoretical modeling, simulation, and machine learning, the research highlights the potential to accelerate progress in IR detection technology and enhance device performance and efficiency. This multidisciplinary methodology could serve as a model for future studies in optoelectronics, illustrating how advanced materials and computational methods can be utilized to enhance device capabilities.