Three-dimensional integrated circuits (3D-ICs) provide a feasible path for scaling CMOS technology in the foreseeable future. IMEC and IRDS roadmaps project that 3D integration is a key avenue for the IC industry beyond 2024. They project that some form of 3D-IC technology based on nanosheets/nanowires is likely to become mainstream soon.

Monolithic 3D IC (M3D) shows degradation in performance compared to 2D IC due to the restricted thermal budget during fabrication of sequential device layers. A transistor-level (TR-L) partitioning design is used in M3D to mitigate this degradation. Silicon validated 14nm FinFET data and models are used in a device-to-system evaluation to compare the TR-L partitioned M3D’s (TR-L M3D) performance against the conventional gate-level (G-L) partitioned M3D’s performance as well as standard 2D IC.

Probabilistic graphical models like Bayesian Networks (BNs) are powerful cognitive-computing formalisms, with many similarities to human cognition. These models have a multitude of real-world applications. New emerging-technology based circuit paradigms leveraging physical equivalence e.g., operating directly on probabilities vs. introducing layers of abstraction, have shown promise in raising the performance and overall efficiency of BNs, enabling networks with millions of random variables.

Thermal management is one of the critical challenges in 3D integrated circuits. Incorporating thermal optimizations during the circuit design stages requires a convenient automatic method of doing thermal characterization for feedback purposes. In this paper, we present a methodology, which supports thermal characterization by automatically extracting the steady-state thermal modeling resistance network from a post-placement physical design. The method follows a two-level hierarchical approach.