Design and Development of MOSFET Under Illumination for Deep Learning Based Biomedical Applications

This study focuses on the design and development of a Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) optimized for optoelectronic biomedical applications under illumination. The proposed MOSFET leverages photon-induced charge carrier generation to enhance its electrical and optoelectronic properties, making it suitable for biomedical sensors, imaging, and diagnostic devices. The device is engineered with advanced materials and fabrication techniques to achieve high photoresponsivity, low dark current, and a fast response time. Under controlled illumination conditions, the developed MOSFET demonstrates a photoresponsivity of 320 A/W, a dark current of 5 nA, and a response time of 12 ns for implementing the deep learning framework to detect the different diseases using ECG waveforms. The threshold voltage shifts under illumination, enhancing its sensitivity and enabling effective operation in low-light environments. The device's performance was validated through experiments involving biomedical sample detection, showing a detection accuracy of 98.2% in real-time ECG diagnosis. These results highlight the potential of the proposed MOSFET for next-generation optoelectronic biomedical applications, paving the way for further advancements in biosensing and medical signal technologies. Future work will explore the integration of the MOSFET with microfluidic systems for lab-on-chip applications, aiming to enhance real-time diagnostics and reduce system complexity.