[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.

[paper] Parameter Extraction of Power MOSFET Models

Michihiro Shintani, Aoi Ueda and Takashi Sato

Accelerating Parameter Extraction of Power MOSFET Models Using Automatic Differentiation

IEEE Transactions on Power Electronics (2021)

DOI:  10.1109/TPEL.2021.3118057

 
Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma (J)

Graduate School of Informatics, Kyoto University (J)
 

Abstract: The extraction of the model parameters is as im- portant as the development of compact model itself because simulation accuracy is fully determined by the accuracy of the pa- rameters used. This study proposes an efficient model-parameter extraction method for compact models of power MOSFETs. The proposed method employs automatic differentiation (AD), which is extensively used for training artificial neural networks. In the proposed AD-based parameter extraction, gradient of all the model parameters is analytically calculated by forming a graph that facilitates the backward propagation of errors. Based on the calculated gradient, computationally intensive numerical differentiation is eliminated and the model parameters are efficiently optimized. Experiments are conducted to fit current and capacitance characteristics of commercially available silicon carbide MOSFET using power MOSFET models having 13 model parameters. Results demonstrated that the proposed method could successfully derive the model parameters 3.50× faster than a conventional numerical-differentiation method while achieving the equal accuracy.

Fig: Backward propagation mode. The dashed arrows indicate the path from E to SCALE.

The propagated values on the path in the backward propagation are highlighted

Acknowledgment: This work was partially supported by JST-OPERA Program Grant Number JPMJOP1841, Japan. The part of this work is also supported by JSPS KAKENHI Grant 20H04156 and 20K21793.