Design of Artificial Intelligence based novel device for fault diagnosis of Integrated Circuits

The rapid advancement of integrated circuit (IC) technology has revolutionised various industries, but it has also introduced challenges in detecting faulty ICs. Traditional testing methods often rely on manual inspection or complex equipment, resulting in time-consuming and costly processes. In this work, a novel approach is proposed which uses a thermal camera and the Internet of Things (IoT) physical device namely Raspberry PI microcontroller for the detection of faulty and non-faulty ICs. Further, a deep learning algorithm namely You Only Look Once (YOLO) is coded inside the Raspberry PI controller using Python programming software to detect faulty ICs efficiently and accurately. Also, the various images of faulty and non-faulty IC are used to train the algorithm and once the algorithm is trained, the thermal camera along with the Raspberry PI microcontroller is used for real-time detection of faulty ICs and the YOLO algorithm analyses the thermal images to identify regions with abnormal temperature patterns, indicating potential faults. The proposed approach offers several advantages over traditional methods, including increased efficiency and improved accuracy.