Silicon Lifecycle Management: From Circuits to Systems

Silicon Lifecycle Management (SLM) has become essential for ensuring reliable and sustainable operation of modern semiconductor systems as technology scaling slows and reliability requirements tighten. Aging mechanisms such as Bias Temperature Instability (BTI), Hot Carrier Injection (HCI), and electromigration now have a first-order impact on performance, energy efficiency, and system lifetime, rendering traditional guardband-based methods increasingly ineffective. This lecture presents a cross-layer view of SLM, spanning aging fundamentals and circuit-level monitoring to architecture- and system-level adaptive management. It highlights emerging machine learning–driven reliability modeling, including graph neural networks (GNNs) for scalable, workload-aware lifetime prediction. The lecture concludes with integration strategies that embed predictive analytics and adaptive control into design-time and runtime frameworks. Attendees will gain a unified understanding of cross-layer reliability modeling and optimization for next-generation, self-aware, and resilient silicon systems.