Design and Development of a Robust Tolerance Optimisation Framework for Automated Optical Inspection in Semiconductor Manufacturing

TL;DR

This paper presents a data-driven framework for optimizing inspection tolerances in Automated Optical Inspection, improving accuracy and reducing false calls in semiconductor manufacturing.

Contribution

It introduces a novel approach using percentile rank and logical strategies to enhance defect detection accuracy and consistency in AOI systems.

Findings

Achieved 18% reduction in false calls at the 80th percentile rank

Maintained 100% defect recall rate

Enhanced efficiency and reliability of AOI processes

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.