Cu–Cu Grain Intermixing Quantification for Hybrid Bonding Applications

Cu–Cu hybrid bonding at reduced thermal budgets depends critically on reliable grain intermixing across the bonding interface. Yet interfacial evolution is still predominantly assessed qualitatively, precluding objective comparison across copper microstructures and process conditions. Here, we introduce Bond IQ, an automated image-based metrology framework that converts registered cross-sectional FIB-SEM datasets into physically interpretable descriptors using classical computer-vision methods. We apply the framework to five blanket chip-level pairings spanning three engineered copper microstructures: coarse-grain (CG), fine-grain locked (FG-L), and fine-grain thermally dynamic (FG-TD) Cu, thermocompression bonded at 210 °C for 2 h. Three coupled but distinct metrics are extracted: Bridged Interface Fraction (BIF), Interface Void Loading (IVL), and normalized Grain Penetration Depth (GPD). The symmetric CG/CG interface exhibited the weakest evolution, combining high IVL (5.4 nm) with low BIF (6.1%) and low GPD (0.20). By contrast, CG/FG-TD achieved the highest BIF (54.5%) at low IVL (0.6 nm), signifying broad but comparatively shallow bridging, whereas CG/FG-L reached the highest GPD (0.89) at moderate BIF (32.7%), indicating localized yet deeper directional grain advancement. Among symmetric fine-grained interfaces, FG-L/FG-L displayed the lowest IVL (0.5 nm), underscoring the role of fine-grain stability in void suppression. Collectively, these results demonstrate that interface bridging, void elimination, and penetration depth are governed by distinct but interrelated contributions of grain-size contrast and microstructural stability. By reducing subjective interfacial assessment to quantitative, mechanistically interpretable descriptors, Bond IQ offers a scalable route to screen copper microstructures and bonding conditions, and it can be extended to other metal–metal interfaces where microstructural evolution governs low-temperature bonding reliability.