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

• URL: http://arxiv.org/abs/2609.17123v1
• Date: Tue, 15 Sep 2026 12:48:44 GMT
• Title: AI for Science with GPT-6 Astra: Thermal Design and Electrothermal Analysis of 2D CFET
• Abstract summary: 2D CFET inverters require testing structural proposals against their electrical costs.<n>We examine these research tasks using an AI agent workflow within a supplied electrothermal model.<n>Results show that an AI scientist workflow can propose thermal structures, test them under common constraints, and quantify their electrical cost.
• Score: 3.6003855507124896
• License: http://creativecommons.org/licenses/by/4.0/
• 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 μW. 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.

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