Automated optical Inspection (AOI) is widely used in the electronics manufacturing industry as a quality inspection process. However, the false call and defect escape can significantly impact throughput and labor involvement for defects verification. It becomes even more challenging with the increasing complexity of electronics designs (e.g., higher density, highly integrated, extreme small or large size, irregular shape components, various materials, and surface features). In recent years, machine learning and artificial intelligence (AI) models are being adopted to help with the pass/fail judgement of AOI machines. However, the accuracy and efficiency enhancement that AI may bring to the AOI is yet to be understood.
Purpose of Project
This project aims to establish common performance metrics to evaluate the AI value-add to AOI capabilities and provide recommendations to the industry on future improvement opportunities. It will include two phases:
Phase 1: Investigation & planning
Phase 2: Experimentation to evaluate AOI+AI capability
This project offers an opportunity to understand the current capability of the AOI+AI solutions in the industry and provides guidelines for the selection of future AI solution scenarios for improving the AOI process. By working together with EMS and OEM users, the AOI equipment vendors can achieve better understanding of what to evaluate and how to apply the AI technology for their existing AOI solutions.
The ultimate goal is to increase the inspection efficiency and accuracy, at the same time, reducing the human resource required for manual inspection verification and mitigating the defect escape risk.
Please note: this is a Fast Turnaround project in which non-member organizations can participate for a one-time participation fee. Contact iNEMI Project Manager Haley Fu (firstname.lastname@example.org) for further details.