As a critical element of integrated circuits, semiconductor chips now demand increasingly higher quality standards. During the miniaturization and high-density manufacturing processes, chips are prone to defects that can impact their performance and longevity. Therefore, detecting and identifying these defects is crucial for enhancing chip reliability. This paper reviews the advancements in chip defect detection methods using machine vision over the past decade, both domestically and internationally. Initially, it introduces the chip manufacturing process and the prevailing chip packaging technologies. It then outlines the mainstream non-destructive testing technologies for chip defect imaging, which include optical imaging, acoustic imaging, infrared thermal imaging, electromagnetic imaging, and X-ray imaging. The paper further explains the methods for detecting surface defects in chips using both traditional technologies and deep learning. Additionally, it compares and analyzes defect detection methods for chip packages based on defect locations. Finally, the paper summarizes the current challenges in chip defect detection and explores potential future research directions