AI-Driven Defect Classification in Advanced Packaging: Lessons from Production Deployments

4:10 pm - 4:30 pm

Advanced packaging has become the critical layer enabling AI compute — from co-packaged optics linking AI accelerators inside data centers, to high-density CIS modules powering AI vision in autonomous systems, to fan-out and 2.5D/3D integration of next-generation devices. Yet as packaging complexity rises, rule-based inspection increasingly fails to keep pace with new defect modes: RDL opens and bridging, bump deformation, TSV voids, die tilt, warpage, and interfacial defects in hybrid bonding. Critical defects escape into high-cost downstream steps, while false rejects from over-tuned recipes erode throughput and operator trust. Inspection is no longer the bottleneck; classification is.

This session draws on production experience across advanced-packaging segments in Taiwan and Singapore: optical interconnects for AI data-center infrastructure, CIS packaging for AI vision and autonomous-system sensors, fan-out packaging, and 2.5D/3D integration including co-packaged optics. Each represents a distinct defect environment, labeling challenge, and integration constraint.

The talk will cover:

Defect taxonomy and labeling strategies across optical-interconnect, fan-out, sensor packaging, and 2.5D/3D process flows
Model design choices for the low-data, high-variability conditions characteristic of new packaging nodes
Deployment patterns for integrating AI-driven classification into existing inspection toolsets and MES/SECS-GEM workflows
Cross-deployment results: reductions in false rejects, recovered operator capacity, and earlier detection of process drift

Attendees will leave with a practical framework for evaluating where AI-driven classification fits within their own advanced-packaging inspection strategy — and a clear-eyed view of what production deployment requires beyond model accuracy.

Key Technologies Covered

AI-Driven Defect Classification for Heterogeneous Integration
Hybrid bonding interfacial defect detection
Self-supervised learning model design for low-data, high-mix packaging environments
Active learning and human-in-the-loop labeling for new defect classes
Transfer learning across packaging segments and process nodes
Multi-modal inspection data fusion (optical, SEM, X-ray)
AI integration with inspection toolsets and MES/SECS-GEM workflows
Anomaly detection for early identification of process drift

Featured Speakers

Ms. Akanksha Jagwani

CEO, SixSense Pte Ltd

Akanksha Jagwani is CEO and co-founder of SixSense AI, an AI platform for semiconductor defect classification, root cause analysis, and yield prediction, with deployments across wafer fabs and advanced-packaging lines in Asia, Europe, and the United States. Since co-founding the company in 2018, she has worked closely with manufacturing partners across Taiwan and Singapore's advanced-packaging ecosystem — including Wavetek (UMC group), Tong Hsing, STATS ChipPAC, and Raytek — to bring AI-driven inspection into production environments. She has been an invited speaker at AI and semiconductor industry conferences in Germany, Singapore, and the United States. She is a graduate of IIT Gandhinagar, a recipient of the Singapore 100 Women in Tech recognition, and was named one of three recipients of IIT Gandhinagar's 2025 Young Alumni Excellence Award for Outstanding Entrepreneurship.

Mr. Rick Lee

Head of Thailand Operations Establishment Office, Raytek Semiconductor Inc.

Rick Lee is a Taiwanese semiconductor executive with 35 years of industry experience, currently serving as Head of Raytek Semiconductor’s Thailand Establishment Office, leading the establishment of Thailand’s first wafer-level interconnection facility. He will serve as President of Raytek Thailand, overseeing the company’s operations and development in Thailand. Prior to joining Raytek, Rick served as Vice President at Siliconware Precision Industries (SPIL), a leading global semiconductor packaging and testing company under ASE Technology Holding (ASEH).