The Device Compact Model Based on Multi-gradient Neural Network and Its Application on MoS<sub>2</sub> Field Effect Transistors

Qi, Guodong; Yang, Qihang; Chen, Xinyu; Zhang, Zengxing; Zhou, Peng; Bao, Wenzhong; Lu, Ye

doi:10.1109/edtm53872.2022.9798306

Cited by 3 publications

(1 citation statement)

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“…Therefore, in this paper, we move away from the conventional CMs and investigate the use of ML, especially, ANNs based CMs for emerging nanodevices [39]- [46]. In recent past many attempts have been made to build fast and accurate CMs with short turnaround time (TAT: refers to the amount of time taken to complete a process) by using ANNs.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

Butola,

Li,

Kola

2023

IEEE Open J. Nanotechnol.

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Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

Butola,

Li,

Kola

2023

IEEE Open J. Nanotechnol.

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A Neural Network-based Manufacturing Variability Modeling of GaN HEMTs

Chavez,

Bavi,

Miller

et al. 2024

2024 IEEE 36th International Conference on Microelectronic Test Structures (ICMTS)

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

Butola,

Li,

Kola

2024

IEEE Trans. Electron Devices

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The Device Compact Model Based on Multi-gradient Neural Network and Its Application on MoS₂ Field Effect Transistors

Cited by 3 publications

References 6 publications

A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

A Neural Network-based Manufacturing Variability Modeling of GaN HEMTs

Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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The Device Compact Model Based on Multi-gradient Neural Network and Its Application on MoS2 Field Effect Transistors

Cited by 3 publications

References 6 publications

A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

A Neural Network-based Manufacturing Variability Modeling of GaN HEMTs

Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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Product

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The Device Compact Model Based on Multi-gradient Neural Network and Its Application on MoS₂ Field Effect Transistors