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

Shonak Bansal; Arpit Jain; Sandeep Kumar; Ashok Kumar; Parvataneni Rajendra Kumar; Krishna Prakash; Mohamed S Soliman; Mohamed Shabiul Islam; Mohammad Tariqul Islam

doi:10.1038/s41598-024-79727-y

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

Sci Rep. 2024 Nov 15;14(1):28230. doi: 10.1038/s41598-024-79727-y.

Authors

Shonak Bansal¹, Arpit Jain², Sandeep Kumar³, Ashok Kumar⁴, Parvataneni Rajendra Kumar⁵, Krishna Prakash⁶, Mohamed S Soliman^{7

8}, Mohamed Shabiul Islam⁹, Mohammad Tariqul Islam¹⁰

Affiliations

¹ Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab, India. shonakk@gmail.com.
² Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundations, Vijayawada, India.
³ School of Computer Science and Artificial Intelligence, SR University, Warangal, India.
⁴ School of Computing, DIT University, Dehradun, Uttarakhand, India.
⁵ Department of AI&ML, NRI Institute of Technology, Agripalli, Eluru, AP, 521212, India.
⁶ Department of Electronics and Communication Engineering, NRI Institute of Technology, Agripalli, Eluru, AP, 521212, India.
⁷ Department of Electrical Engineering, College of Engineering, Taif University, 21944, Taif, Saudi Arabia.
⁸ Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan, 81528, Egypt.
⁹ Faculty of Engineering (FOE), Multimedia University (MMU), 63100, Cyberjaya, Selangor, Malaysia. shabiul.islam@mmu.edu.my.
¹⁰ Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, UKM, 43600, Bangi, Selangor, Malaysia. tariqul@ukm.edu.my.

Abstract

This research explores the design of an infrared (IR) photodetector using mercury cadmium telluride (Hg_1-xCd_xTe). It proposes two- and three-dimensional homojunction models based on p⁺-Hg_0.7783Cd_0.2217Te/n^--Hg_0.7783Cd_0.2217Te, 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/cm², photocurrent density of 4.98 A/cm², and photocurrent density-to-dark current density ratio of 2.46 × 10⁴, 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 × 10¹¹ 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.

Keywords: HgCdTe; Homojunction; Infrared photodetector; Long-wavelength infrared (LWIR); Machine learning; Noise equivalent power; Optoelectronic; Photodetector; Quantum efficiency; Regression; Silvaco TCAD.