Automated Optical Inspection: Implications on Board Assembly
Key Summary
• iNEMI launched an industry-wide survey on AI adoption in automated optical inspection (AOI) for board assembly
• The survey aims to assess current technology use, challenges, and lessons learned across companies
• It will identify common defect modes and component types detected by AOI systems
• Survey results will guide future AI solution development and shape the next phase of iNEMI’s AOI enhancement project
• Input is requested from all companies, including those not planning to adopt AI in AOI
The International Electronics Manufacturing Initiative (iNEMI) is conducting an industry-wide survey to assess the development of artificial intelligence (AI) and its implementation for automated optical inspection (AOI) in board assembly.
This survey focuses on the status of technology adoption. If your company has no plans to adopt AI in AOI process, we would like to know that, too. The survey looks to highlight common challenges or capture lessons learned for future development. It will also identify the defect modes and component types most commonly seen from AOI.
Results from this survey will help define future AI solution scenarios for improving the AOI process. It will also inform the definition of an experiment plan for the next phase of iNEMI’s AI Enhancement to AOI for PCBA project. For additional information, contact Haley Fu (haley.fu@inemi.org).
Preview the survey questionnaire.
Go to the online survey.
DEADLINE FOR INPUT: Saturday, May 14, 2022
The survey helps evaluate how widely AI technologies are being adopted in AOI processes, what challenges companies face, and what improvements are needed. Insights will support the development of more effective AI-driven inspection solutions.
Findings will inform AI solution scenarios and help define experiment plans for iNEMI’s next AOI enhancement phase. This ensures research aligns with real manufacturing needs and industry-wide defect patterns.
Yes. Understanding why some companies are not adopting AI is as important as learning from those who are. This feedback helps identify barriers, readiness levels, and potential support needed across the industry.
It targets current adoption levels, challenges, lessons learned, defect modes frequently detected by AOI, and which component types present the most inspection difficulty. These insights shape practical AI improvements.
They can preview the questionnaire or access the online survey through the provided links. For further details, participants may contact iNEMI’s project lead, Haley Fu, for guidance or support.