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Zhiang Wang

Publications and source records attributed to Zhiang Wang.

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Differentiable Partitioning with Placement and Hybrid Bonding Terminal Awareness for Optimized 3D Placement

Research on 3D-ICs physical design has expanded rapidly in recent years. Hybrid bonding-enabled 3D integrated circuits (3D-ICs) offer substantial benefits in interconnect scaling and system integration, yet tier assignment remains challenging because it jointly determines 3D wirelength and hybrid bonding terminal (HBT) assignment. This paper presents a differentiable partitioning framework that directly optimizes placement-aware tier assignment for 3D-ICs through gradient-based optimization. Discrete tier assignment is relaxed to continuous probabilities, and a Dual-Max 3D wirelength model is introduced to capture per-tier half-perimeter wirelength (HPWL). In addition, a terminal-aware cutsize penalty selectively suppresses cross-die nets in HBT-congested regions, and a local balance constraint enforces grid-cell density equilibrium across tiers. Experimental results on OpenROAD benchmarks show that our method reduces D2D HPWL by 2.0% on average over two min-cut baselines and by 12.1% over the state-of-the-art 3D placer. We open-source our partition code with 3D placement flow to support reproducibility.

cs.ET

AutoDRI: Bridging the Semantic Gap for Automated Design Rule Integration in CP-SAT-Based Cell Synthesis under Multi-Patterning

Design-rule integration (DRI) remains a major bottleneck for scalable (Constraint Programming with SAT) CP-SAT-based standard cell synthesis and rapid technology enablement at advanced nodes. It still depends heavily on manual effort and domain expertise. Moreover, existing low-level rule encodings are not expressive enough for emerging constraints such as cut-based rules under multi-patterning technology. This paper presents \textbf{AutoDRI}, a multi-agent framework for automated design-rule integration in standard cell synthesis. AutoDRI combines a geometric semantic library, a standardized conflict-set encoding, a constructive multicolor-cut modeling method, and a feedback-driven multi-agent flow to bridge the semantic gap between natural-language design rules and executable CP-SAT constraints. In the reported experiments, AutoDRI achieves near-perfect rule-integration correctness across 41 cell benchmarks under 10+ complex rules, including colored cut-mask spacing rules, reaching 33/33 correct integrations with Gemini-3-pro and 32/33 with GPT-5.4, while maintaining runtime comparable to manual hard-coding and passing KLayout DRC and Cadence LVS.

eess.SY