AI for Science with GPT-6 Astra: Thermal Design and Electrothermal Analysis of 2D CFET

Abstract:Thermal optimization of 2D CFET inverters requires testing structural proposals against their electrical costs. We examine these research tasks using an AI agent workflow within a supplied electrothermal model. At 12 nm, Astra selects a redistributed source-interconnect geometry, while a coordinating agent proposes a substrate-directed heat-removal path. The combined design reduces peak temperature rise by 1.67 K at fixed metal volume and 20 {\mu}W. A subsequent metal-resistance sensitivity gives about 0.6-K inverter cooling alongside a 2% nFET on-current loss. Effective contact-length scaling further shows that lower temperature can accompany higher thermal resistance when current falls. Reproduction identifies agreeing implementations and retains a 104.95-K failure for diagnosis. These results show that an AI scientist workflow can propose thermal structures, test them under common constraints, and quantify their electrical cost.

Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)

arXiv:2609.17123 [cond-mat.mtrl-sci]

(or arXiv:2609.17123v1 [cond-mat.mtrl-sci] for this version)

https://doi.org/10.48550/arXiv.2609.17123 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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