用大模型自动生成半导体器件代码，4小时完成人需7天的工作
AgenticTCAD: A LLM-based Multi-Agent Framework for Automated TCAD Code Generation and Device Optimization
- 构建多智能体系统，通过自然语言指令自动设计与优化器件
- 在2纳米晶体管上4.2小时内达成国际器件路线图标准
- 适合芯片设计与仿真领域研究者快速原型验证
随着先进制程节点持续演进，设计-工艺协同优化（DTCO）愈发关键，高效器件设计与优化成为必要。然而，在TCAD仿真领域，开源资源匮乏限制了语言模型生成有效代码的能力。为此，我们构建了一个由专家整理的开源TCAD数据集，并微调出面向TCAD代码生成的专用模型。在此基础上，提出AgenticTCAD——一个以自然语言驱动的多智能体框架，实现端到端的自动化器件设计与优化。在2纳米片式晶体管（NS-FET）设计验证中，AgenticTCAD仅用4.2小时即达到国际器件与系统路线图（IRDS-2024）要求，而人类专家使用商用工具需7.1天。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。