A Survey of Detection Methods for Die Attachment and Wire Bonding Defects in Integrated Circuit Manufacturing

TL;DR

This survey reviews various sensing modalities and approaches, including deep learning, for detecting die attachment and wire bonding defects in IC manufacturing, highlighting current methods, challenges, and future research directions.

Contribution

It provides a comprehensive overview of detection techniques for IC defects, integrating conventional and deep learning approaches, and discusses future research challenges.

Findings

Optical, radiological, acoustical, and infrared thermography are used for defect detection.

Deep learning methods are increasingly applied to improve detection accuracy.

The survey identifies key challenges and future directions in defect detection.

Abstract

Defect detection plays a vital role in the manufacturing process of integrated circuits (ICs). Die attachment and wire bonding are two steps of the manufacturing process that determine the power and signal transmission quality and dependability in an IC. This paper presents a survey or literature review of the methods used for detecting these defects based on different sensing modalities used including optical, radiological, acoustical, and infrared thermography. A discussion of the detection methods used is provided in this survey. Both conventional and deep learning approaches for detecting die attachment and wire bonding defects are considered along with challenges and future research directions.