Chiplet Packaging and AI Defect Inspection

ABSTRACT The semiconductor industry is undergoing a fundamental transformation as system architects increasingly adopt heterogeneous integration to sustain performance scaling, reduce power consumption, and achieve unprecedented design flexibility. Chiplet based architectures enabled through advanced hybrid bonding provide a compelling alternative to monolithic integration by allowing diverse functional blocks to be independently fabricated, optimized, and integrated within a single package. Hybrid bonding delivers ultra fine pitch interconnects, superior electrical characteristics, and reduced parasitic losses, yet its precision driven processes introduce complex reliability and manufacturability challenges. Defect formation during wafer to wafer and die to wafer hybrid bonding continues to limit yield and long-term interfacial integrity due to surface particulates, topography mismatches, void formation, and nanoscale misalignment [1]. This manuscript offers a detailed examination of chiplet integration strategies, hybrid bonding principles, and the emerging landscape of artificial intelligence enabled inspection methodologies. Machine learning driven frameworks provide powerful capabilities for detecting subtle defect signatures, classifying interfacial anomalies, and predicting reliability outcomes by correlating pre bond and post bond process data. Such data centric approaches facilitate real time process control, early risk identification, and adaptive optimization throughout the packaging workflow. The discussion highlights how the synergy between chiplet architectures, hybrid bonding, and artificial intelligence-based inspection is poised to significantly elevate manufacturing yield, alignment accuracy, and overall reliability, thereby accelerating the path toward next generation high performance electronic systems.