https://www.matjournals.net/engineering/index.php/RTSST/issue/feedRecent Trends in Semiconductor and Sensor Technology2026-10-23T11:14:27+00:00Open Journal Systems<p>Recent Trends in Semiconductor and Sensor Technology is a peer-reviewed journal in the field of Electronics published by the MAT Journals Pvt. Ltd. RTSST is print e-journal focused towards the rapid Publication of fundamental research papers on all areas of Semiconductor and Sensor Technology. This Journal involves the basic principles of Emerging trends and developments in semiconductor materials and devices, Novel sensor technologies and their applications in various fields, Micro- and nano electronics and their applications, Integration of sensors with different technologies, such as IoT, AI, and wearable devices, Fabrication techniques and processes in semiconductor technology, Signal processing and data analysis methods for sensors and semiconductor devices. The Journal aims to promote high-quality Research, Review articles, and case studies mainly focussed on Miniaturization and Micro electromechanical Systems (MEMS), Internet of Things (IoT), sensor integration, fabrication techniques, Quantum computing, multi-sensor systems, CMOS, Optical Sensors, and Light Emitters,and sensor fusion. This Journal involves the comprehensive coverage of all the aspects of Semiconductor and Sensor Technology.</p>https://www.matjournals.net/engineering/index.php/RTSST/article/view/4342Deep Learning Framework for Real-Time PCB Defect Detection and Automated Segregation2026-10-23T11:01:06+00:00Chiranthan Gowda S. P.chiranthangowdasp@gmail.comMohammed Affanchiranthangowdasp@gmail.comPughal P.chiranthangowdasp@gmail.comVeeresha M.chiranthangowdasp@gmail.comChethan K. S.chiranthangowdasp@gmail.com<p><em>Quality control in semiconductor and Printed Circuit Board (PCB) production demands inspection methods that are fast, consistent, and capable of identifying small surface-level faults. Conventional manual checking is difficult to scale and may lead to missed defects when production volume is high. Recent research indicates that computer-vision models, particularly YOLO-based detectors, are suitable for real-time industrial inspection because they can locate and classify objects in a single processing stage. This paper reviews recent work on PCB defect detection, semiconductor inspection, and e-waste sorting, and presents a proposed system that combines YOLOv8, camera input, a Raspberry Pi 4, and a motor-driven conveyor arrangement. The review identifies three gaps in existing work: the lack of a link between detection and physical action, the limited attention to low-cost embedded deployment, and the absence of a reusability decision after defect detection. As the hardware prototype and testing phase are still in progress, the manuscript reports the review findings, system objectives, proposed workflow, expected outcomes, and implementation challenges without claiming experimental results. The proposed system classifies components as reusable or non-reusable and supports automatic segregation for smart manufacturing and e-waste handling. This is a review article with a proposed framework; reusability criteria and an evaluation plan are defined for later experimental validation.</em></p>2026-10-23T00:00:00+00:00Copyright (c) 2026 Recent Trends in Semiconductor and Sensor Technology