# AgenticTCAD: A LLM-based Multi-Agent Framework for Automated TCAD Code Generation and Device Optimization

arXiv: https://arxiv.org/abs/2512.23742 · HTML: https://mlsanity.com/paper/2512.23742/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 26 Dec 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 12 of 12)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [ChatEDA: A Large Language Model Powered Autonomous Agent for EDA](https://mlsanity.com/paper/2308.10204/) (2023)
- [RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models](https://mlsanity.com/paper/2311.16543/) (2023)
- [OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection](https://mlsanity.com/paper/2407.16237/) (2024)
- [ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models](https://mlsanity.com/paper/2406.18770/) (2024)
- [RTLRewriter: Methodologies for Large Models aided RTL Code Optimization](https://mlsanity.com/paper/2409.11414/) (2024)
- [MEIC: Re-thinking RTL Debug Automation using LLMs](https://mlsanity.com/paper/2405.06840/) (2024)
- [LayoutCopilot: An LLM-powered Multi-agent Collaborative Framework for Interactive Analog Layout Design](https://mlsanity.com/paper/2406.18873/) (2024)
- [A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA](https://mlsanity.com/paper/2504.03711/) (2025)
- [Large Language Model (LLM) for Standard Cell Layout Design Optimization](https://mlsanity.com/paper/2406.06549/) (2024)

## Built on by (most cited first, 2 of 2)

- [Agentic Electronic Design Automation: A Handoff Perspective](https://mlsanity.com/paper/2606.19795/) (2026)
- [Agentic TCAD Calibration Workflow for Oxide Semiconductor Transistors](https://mlsanity.com/paper/2609.12184/) (2026)

## Abstract (arXiv)

With the continued scaling of advanced technology nodes, the design-technology co-optimization (DTCO) paradigm has become increasingly critical, rendering efficient device design and optimization essential. In the domain of TCAD simulation, however, the scarcity of open-source resources hinders language models from generating valid TCAD code. To overcome this limitation, we construct an open-source TCAD dataset curated by experts and fine-tune a domain-specific model for TCAD code generation. Building on this foundation, we propose AgenticTCAD, a natural language - driven multi-agent framework that enables end-to-end automated device design and optimization. Validation on a 2 nm nanosheet FET (NS-FET) design shows that AgenticTCAD achieves the International Roadmap for Devices and Systems (IRDS)-2024 device specifications within 4.2 hours, whereas human experts required 7.1 days with commercial tools.