arXiv subjects

Yuhao Ren

Publications and source records attributed to Yuhao Ren.

5 recordsLinked to original sources

arXiv2026

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↗

arXiv2026

GoalEvolve: From Handcrafted Algorithm Priors to Goal-Driven Evolution of Physical Design Algorithms

Physical design algorithms operate within tightly coupled, multi-stage optimization flows, where stage-local gains may vanish or induce downstream degradation. Existing program-evolution frameworks often rely on stage-local objectives or undifferentiated multi-metric feedback, which neither guarantee better final results nor identify which unmet requirement should guide the next iteration. We present GoalEvolve, a goal-driven framework that makes physical design algorithm evolution accountable for the final quality of results (QoR) of the complete flow. Given a multi-objective QoR target region, GoalEvolve converts unmet requirements into normalized target gaps, identifies the dominant bottleneck, and uses stage-resolved checkpoint evidence to locate the responsible stage. An LLM-based Teacher then narrows the search to a relevant algorithmic decision and source region, while parallel Student agents implement and validate hypotheses through full-flow evaluation. Local effects, optimization debt, and downstream retention are retained as mechanism evidence for subsequent evolution. Across eight ASAP7 designs, GoalEvolve improves post-route TNS by 30.67% on average and reduces leakage and dynamic power by 21.18% and 9.42% versus default OpenROAD. Relative to commercial-tool goals, it closes 62.20% of the normalized power gap on power-dominant designs, surpasses the TNS goals on both timing-dominant designs, and closes 32.48% of the equal-weight timing-power gap on joint designs. Across all three designs evaluated against Codex goal mode under matched budgets, GoalEvolve further improves TNS by 26.46% while reducing leakage and dynamic power by 12.38% and 0.76%, respectively.

cs.AR↗

arXiv2025

Bridging the Initialization Gap: A Co-Optimization Framework for Mixed-Size Global Placement

Global placement is a critical step with high computational complexity in VLSI physical design. Modern analytical placers formulate the placement problem as a nonlinear optimization, where initialization strongly affects both convergence behavior and final placement quality. However, existing initialization methods exhibit a trade-off: area-aware initializers account for cell areas but are computationally expensive and can dominate total runtime, while fast point-based initializers ignore cell area, leading to a modeling gap that impairs convergence and solution quality. We propose a lightweight co-optimization framework that bridges this initialization gap through two strategies. First, an area-hint refinement initializer incorporates heuristic cell area information into a signed graph signal by augmenting the netlist graph with virtual nodes and negative-weight edges, yielding an area-aware and spectrally smooth placement initialization. Second, a macro-schedule placement procedure progressively restores area constraints, enabling a smooth transition from the refined initializer to the full area-aware objective and producing high-quality placement results. We evaluate the framework on macro-heavy ISPD2005 academic benchmarks and two real-world industrial designs across two technology nodes (12 cases in total). Experimental results show that our method consistently improves half-perimeter wirelength (HPWL) over point-based initializers in 11 out of 12 cases, achieving up to 2.2% HPWL reduction, while running approximately 100 times faster than the state-of-the-art area-aware initializer.

eess.SP↗

arXiv2023

A Kriging-Random Forest Hybrid Model for Real-time Ground Property Prediction during Earth Pressure Balance Shield Tunneling

A kriging-random forest hybrid model is developed for real-time ground property prediction ahead of the earth pressure balanced shield by integrating Kriging extrapolation and random forest, which can guide shield operating parameter selection thereby mitigate construction risks. The proposed KRF algorithm synergizes two types of information: prior information and real-time information. The previously predicted ground properties with EPB operating parameters are extrapolated via the Kriging algorithm to provide prior information for the prediction of currently being excavated ground properties. The real-time information refers to the real-time operating parameters of the EPB shield, which are input into random forest to provide a real-time prediction of ground properties. The integration of these two predictions is achieved by assigning weights to each prediction according to their uncertainties, ensuring the prediction of KRF with minimum uncertainty. The performance of the KRF algorithm is assessed via a case study of the Changsha Metro Line 4 project. It reveals that the proposed KRF algorithm can predict ground properties with an accuracy of 93%, overperforming the existing algorithms of LightGBM, AdaBoost-CART, and DNN by 29%, 8%, and 12%, respectively. Another dataset from Shenzhen Metro Line 13 project is utilized to further evaluate the model generalization performance, revealing that the model can transfer its learned knowledge from one region to another with an accuracy of 89%.

cs.LG↗

arXiv2022

Low-threshold nanolasers based on miniaturized bound states in the continuum

The pursuit of compact lasers with low-thresholds has imposed strict requirements on tight light confinements with minimized radiation losses. Bound states in the continuum (BICs) have been recently demonstrated as an effective mechanism to trap light along the out-of-plane direction, paving the way to low-threshold lasers. To date, most reported BIC lasers are still bulky due to the absence of in-plane light confinement. In this work, we combine BICs and photonic band gaps to realize three-dimensional (3D) light confinements, as referred to miniaturized (mini-) BICs. Together with 3D carrier confinements provided by quantum dots (QDs) as optical gain materials, we have realized highly-compact active BIC resonators with a record-high quality ($Q$) factor up to 32500, which enables single-mode continuous wave (CW) lasing with the lowest threshold of 80 W/cm$^{2}$ among the reported BIC lasers. In addidtion, our photon statistics measurements under both CW and pulsed excitations confirm the occurence of the phase transition from spontaneous emission to stimulated emission, further suggesting that conventional criteria of input-output and linewidth are not sufficient for claiming nanoscale lasing. Our work reveal a via path towards compact BIC lasers with ultra-low power consumption and potentially boost the applications in cavity quantum electrodynamics (QEDs), nonlinear optics and integrated photonics.

physics.optics↗