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

arXiv: https://arxiv.org/abs/2609.17123 · HTML: https://mlsanity.com/paper/2609.17123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 2 of 2)

- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [A Novel Thermal Network Model and Electro-Thermal Coupling Study for NSFETs and CFETs Considering Thermal Crosstalk](https://mlsanity.com/paper/2506.12264/) (2025)

## Abstract (arXiv)

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.