A new AI-accelerated iterative solver, DAIST, is introduced for 3D-IC thermal modeling. It decomposes a full-package FEM simulation into block-level subdomains, replaces each subdomain solver with a neural operator, and couples them through iterative exchanges of interfacial temperature and heat flux. This approach eliminates the need to retrain models when die count or topology changes and offers a controllable accuracy‑runtime tradeoff, achieving up to 178× speedup with minimal temperature error on tested systems.
By Yixing Li, Jiahang Zhou, Zhiyu Zeng, Xin Ai
arXiv:2603. 29268v2 Announce Type: replace Abstract: High-density through-substrate vias (TSVs) enable 2.
By Mohamed Gharib, Leonid Popryho, Inna Partin-Vaisband
arXiv:2608. 16080v1 Announce Type: new Abstract: Thermal-aware optimization of multi-die 3D integrated circuits evaluates many designs, each a costly heat-equation solve.
By Xinling Yu, Yixing Li, Ziyue Liu, Xin Ai, Zhiyu Zeng, Hai Li, Zheng Zhang
The paper introduces the Tree-Structured Factor Composition Network (TFCN), a surrogate model that decomposes spacecraft thermal configurations into reusable local physical factors and learns their global temperature-field response via a tree-structured composition module. Trained on configurations with up to 15 heat-generating components, TFCN is evaluated on unseen setups with 16–25 components, achieving out-of-distribution RMSE reductions of 65.6% and 33.6% compared to the best baseline for prescribed-temperature and radiative-flux boundary conditions, respectively. These results demonstrate that TFCN can reliably predict temperature fields across varying component counts, enabling efficient rapid evaluation and large-scale screening in spacecraft thermal design.
By Qineng Wang, Xinrui Zhou, Shuwen Yue, Kangli Bao, Hairun Xie, Yonghe Zhang
The paper introduces a new EnergyPlus object, ElectricEquipment:ITE:LiquidCooled, that models direct-to-chip liquid cooling for both chilled‑water and warm‑water data center architectures. Validation against a Modelica implementation shows temperature errors below 0.021 K across 21 operating conditions, meeting the 0.1 K threshold. A case study indicates that warm‑water cooling can lower annual PUE from 1.43 to 1.25, eliminate on‑site water use, and cut total facility energy by 37.9% compared to an air‑cooled baseline.
By Yun-Dam Ko, Dipashreya A. Sur, Cade Napier, Carlos D. D\'iaz-Mar\'in, Rishee K. Jain
The paper introduces an AI-driven framework for thermal‑aware capacity planning in data centers, enabling temperature predictions in milliseconds by learning from key parameters such as rack power, server placement, and HVAC settings. The model is validated against high‑fidelity CFD simulations and achieves a 10,000‑fold speedup while maintaining high accuracy for unseen designs. This rapid prediction capability allows designers and operators to instantly optimize workload distribution and cooling efficiency.
By Yixing Li, Mark Fenton, Matthew Kaufeler, Ka Ming Leung, Xin Ai, Zhiyu Zeng
arXiv:2608. 10344v1 Announce Type: cross Abstract: Thermo-mechanical compliant devices are commonly designed with small-strain linear elasticity and temperature-independent material properties, even though they might operate hundreds of kelvin above ambient where both assumptions are questionable.
By Shirin Hosseinmardi, Xiangyu Sun, Ramin Bostanabad
AnalogScout is a new framework that uses a large language model (LLM) agent to perform PVT-aware analog device sizing more efficiently. By reusing elite designs, applying analytic gm/Id local sizing, and refining globally under full-corner verification, it cuts power consumption by 43.3% compared to the CMA-ES evolution strategy while using the same 16,000 simulations on three SKY130 OTAs. The method also outperforms Bayesian optimization and other LLM-driven flows on the tested OTAs.
By Yichen Long, Keren Zhu
arXiv:2510. 15968v2 Announce Type: replace-cross Abstract: Thermal management in 3D ICs is increasingly challenging due to higher power densities.
By Zhen Huang, Hong Wang, Wenkai Yang, Muxi Tang, Depeng Xie, Ting-Jung Lin, Yu Zhang, Wei W. Xing, Lei He
EGGPlacer is an energy‑guided diffusion framework for macro placement that integrates analytical placement feedback and differentiable thermal feedback. By treating these signals as composable energies, the method injects them into reverse diffusion without retraining the generative model. Experiments on public benchmarks show that EGGPlacer achieves competitive mixed‑size HPWL and outperforms other methods in routed wirelength and peak temperature.
By Qi Zhou, Guojun Liu, Juanhong Zhao, Guangzhi Qi, Zehao Chen, Ming Lu
arXiv:2607. 27251v1 Announce Type: new Abstract: Transformer-based surrogate models are increasingly used to replace expensive first-principles simulation in engineering design.
By Kart-leong Lim
arXiv:2606. 01265v1 Announce Type: cross Abstract: This paper demonstrates the effectiveness of machine learning-driven optimization for designing application-specific GaN tri-gate FinFETs in vertical power delivery systems.
By Ayoub Sadeghi, Leonid Popryho, Inna Partin-Vaisband