[paper] ATMAD: Compact Modeling with Parameter Extraction

Yuhang Zhang, Qing Zhang, Yang Shen, Bingyi Ye, Xiaojin Li, Yabin Sun, Yanling Shi, Yong‑Fu Li

ATMAD: Agile Transistor Compact Modeling with Parameter Extraction 

Based on Automatic Differentiation

ACM 1084-4309/2026/04-ART103

DOI: 10.1145/3797484

1. School of Integrated Circuits, East China Normal University, Shanghai, China
2. Department of Micro‑Nano Electronics, Shanghai Jiao Tong University, Shanghai, China
3. East China Normal University, Shanghai, China
4. Shanghai Jiao Tong University, Shanghai, China

Abstract: Compact models of transistors are essential for simulating and optimizing circuits with the use of SPICE simulation tool. Parameter extraction, which is calibrating these models, is essential to ensure their alignment with measured or simulated data. However, conventional parameter extraction methods are generally iterative and experience-dependent, requiring significant time and effort from modeling engineers. Moreover, as semiconductor devices and compact models become increasingly advanced, the need for a tailored extraction process for each model has become increasingly inefficient. To address the above challenges, this work proposes an agile transistor compact modeling framework, ATMAD. The proposed framework takes a compact model file and a set of electrical characteristic data as inputs, producing a calibrated model with minimal human intervention. ATMAD automatically retrieves the equations in the compact model and converts them into computational flow graphs, thus supporting different compact models with a generalized process. A graph unlooping technique is proposed to support automatic differentiation for compact models with implicit functions (e.g., series resistance and surface potential solving). Based on the computational flow graph, ATMAD adopts automatic differentiation technique to achieve automatic and parallel optimization of model parameters. The proposed ATMAD framework is validated on commonly-used compact models in academia and industry, showing its effectiveness for compact modeling for both 𝐼𝑉 and 𝐶𝑉 characteristics.

Fig. The overall flow of ATMAD framework

Acknowledgements: This work is supported in part by the Shanghai Explorer Program under Grant No. 25TS1410300 and 24TS1400200 and in part by the National Natural Science Foundation of China under Grants No. 62304133 and 62350610271.