MALTS: A Multi-Agent Large Language Model-Based Layout-to-TCAD Structure Synthesizer

The demand for efficient 3D modeling in Technology Computer-Aided Design (TCAD) simulations has surged, driven by the need for rapid device performance evaluation and Design Technology Co-Optimization (DTCO). Traditional TCAD modeling involves time-consuming process emulation and complex script editing, which limits the scope within single-device simulations and hinders scalability. To address these challenges, this paper introduces MALTS, a multi-agent large language model-based layout-to-TCAD structure synthesizer. The proposed framework takes industrial layout files (e.g., GDSII) and process information as inputs, and directly generates simulation-ready definitions in standard TCAD syntax, eliminating the need for iterative process emulation and manual script adjustments typically required in conventional workflows. By decomposing the synthesis process and assigning each phase to a specialized LLM agent, MALTS mitigates the "long-context forgetting" problem, enabling efficient generation of complex structures and gate-level TCAD models. Experimental results demonstrate that the proposed framework provides an automated solution for analyzing the impact of various physical layout designs and process details on electrical characteristics, facilitating rapid design space exploration in the DTCO process.

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Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1109/iseda65950.2025.11101083
Primary Topic
Model-Driven Software Engineering Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

MALTS: A Multi-Agent Large Language Model-Based Layout-to-TCAD Structure Synthesizer

Bingyi Ye, Yabin Sun, Jun Dou, Yanling Shi et al.
Model-Driven Software Engineering Techniques
article

MALTS: A Multi-Agent Large Language Model-Based Layout-to-TCAD Structure Synthesizer

Bingyi Ye, Yabin Sun, Jun Dou, Yanling Shi, Yuhang Zhang, Yang Shen, Xiaojin Li, Jianing Zhang
article en

Abstract

The demand for efficient 3D modeling in Technology Computer-Aided Design (TCAD) simulations has surged, driven by the need for rapid device performance evaluation and Design Technology Co-Optimization (DTCO). Traditional TCAD modeling involves time-consuming process emulation and complex script editing, which limits the scope within single-device simulations and hinders scalability. To address these challenges, this paper introduces MALTS, a multi-agent large language model-based layout-to-TCAD structure synthesizer. The proposed framework takes industrial layout files (e.g., GDSII) and process information as inputs, and directly generates simulation-ready definitions in standard TCAD syntax, eliminating the need for iterative process emulation and manual script adjustments typically required in conventional workflows. By decomposing the synthesis process and assigning each phase to a specialized LLM agent, MALTS mitigates the "long-context forgetting" problem, enabling efficient generation of complex structures and gate-level TCAD models. Experimental results demonstrate that the proposed framework provides an automated solution for analyzing the impact of various physical layout designs and process details on electrical characteristics, facilitating rapid design space exploration in the DTCO process.

East China Normal University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 79%
Model-Driven Software Engineering Techniques
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