Ultra-Low Latency Edge Analytics and Cyber-Physical Digital Twins for Closed-Loop Quality Assurance in High-Speed Semiconductor Packaging

• Ada John

  ladoke Akintola university of technology

  Author

High-speed semiconductor packaging faces increasing yield challenges as defect modes in advanced packaging architectures become concealed and unobservable through traditional inspection methods. Existing quality assurance systems operate as open-loop monitoring frameworks with latency exceeding actionable correction windows, fundamentally limiting their capacity for real-time process intervention. This study designs and evaluates a cyber-physical digital twin architecture integrating ultra-low latency edge analytics with closed-loop feedback control for semiconductor packaging quality assurance. The research employs a design-based methodology incorporating retrospective analysis of 2,847 packaging process records and prospective simulation of edge inference performance across 12 defect scenarios. A hybrid machine learning framework combining physics-informed neural networks with lightweight convolutional architectures was deployed on edge hardware to enable sub-50ms inference latencies. Results demonstrate 89.4% defect prediction accuracy with 34ms average end-to-end latency, representing a 2.6x improvement over cloud-based baselines while maintaining 8.9W power consumption. The closed-loop architecture achieved 31% reduction in mean time-to-correction compared to advisory-only digital twin implementations. The findings establish that latency-constrained edge analytics coupled with cyber-physical feedback mechanisms can transform semiconductor packaging quality assurance from reactive inspection toward predictive process control, with implications for yield optimization in heterogeneous integration manufacturing.

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Copyright (c) 2026 Ada John (Author)

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