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 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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BibTeX RIS

BibTeX

@misc{kim2026science,
  title = {{AI for Science with GPT-6 Astra: Thermal Design and Electrothermal Analysis of 2D CFET}},
  author = {Min-Hui Kim and Khushi Sharma and Sarah Zhang and Ye Wang},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.17123},
  eprint = {2609.17123},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.17123},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - AI for Science with GPT-6 Astra: Thermal Design and Electrothermal Analysis of 2D CFET
AU  - Min-Hui Kim
AU  - Khushi Sharma
AU  - Sarah Zhang
AU  - Ye Wang
PY  - 2026
DA  - 2026/09/15
PB  - arXiv
DO  - 10.48550/arxiv.2609.17123
UR  - https://arxiv.org/abs/2609.17123
AB  - 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.
ER  -

Abstract

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