Abstract: Integrated circuits are an important part of electronic products, and their quality control and fault analysis are prerequisites for the long-term operation of electronic products. Scanning Acoustic Microscope (SAM), as a non-destructive defect detection method, has been widely used in imaging detection and internal defect identification of integrated circuits. In response to the intelligent demand for acoustic scanning image defect detection and the requirements for real-time and accurate detection, this paper develops a software for integrated circuit acoustic scanning image defect detection based on machine vision, providing integrated functions for image processing and image detection. The algorithm framework of this software combines deep learning technology, traditional image processing technology using OpenCV, and JavaScript interface design technology, allowing it to manage various types of integrated circuit data and analyze, process, and determine defects in scanning acoustic images.

Key words: scanning acoustic image, defect detection, object detection network, JavaScript