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.
翻译:随着先进工艺节点的持续微缩,设计-技术协同优化(DTCO)范式变得日益关键,使得高效的器件设计与优化至关重要。然而,在TCAD仿真领域,开源资源的匮乏阻碍了语言模型生成有效的TCAD代码。为克服这一局限,我们构建了一个由专家筛选的开源TCAD数据集,并微调了一个面向TCAD代码生成的领域专用模型。在此基础上,我们提出了AgenticTCAD,一个由自然语言驱动的多智能体框架,能够实现端到端的自动化器件设计与优化。在2纳米纳米片场效应晶体管(NS-FET)设计上的验证表明,AgenticTCAD可在4.2小时内达到国际器件与系统路线图(IRDS)-2024的器件规格要求,而人类专家使用商业工具则需要7.1天。