Preprint on Robust Tolerance Optimisation Framework for AOI in Semiconductor Manufacturing

Shruthi’s work on developing a robust tolerance optimisation framework for Automated Optical Inspection (AOI) in semiconductor manufacturing is now out as a preprint on arXiv. This work is also our first publication in the Knowledge Transfer Program (KTP) project with Elite Electronic Systems as the industrial partner. The KTP focuses on tackling the complex task of building a smart sensor and data analysis system for fault detection in a Surface Mount Technology (SMT) manufacturing process, leading to a Digital Twin to enhance yield reliability and quality.

Abstract: Automated Optical Inspection (AOI) is widely used across various industries, including surface mount technology in semiconductor manufacturing. One of the key challenges in AOI is optimising inspection tolerances. Traditionally, this process relies heavily on the expertise and intuition of engineers, making it subjective and prone to inconsistency. To address this, we are developing an intelligent, data-driven approach to optimise inspection tolerances in a more objective and consistent manner. Most existing research in this area focuses primarily on minimising false calls, often at the risk of allowing actual defects to go undetected. This oversight can compromise product quality, especially in critical sectors such as medical, defence, and automotive industries. Our approach introduces the use of percentile rank, amongst other logical strategies, to ensure that genuine defects are not overlooked. With continued refinement, our method aims to reach a point where every flagged item is a true defect, thereby eliminating the need for manual inspection. Our proof of concept achieved an 18% reduction in false calls at the 80th percentile rank, while maintaining a 100% recall rate. This makes the system both efficient and reliable, offering significant time and cost savings.