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Computer Science > Machine Learning
Title: improving semiconductor device modeling for electronic design automation by machine learning techniques.
Abstract: The semiconductors industry benefits greatly from the integration of Machine Learning (ML)-based techniques in Technology Computer-Aided Design (TCAD) methods. The performance of ML models however relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device fabrication. In this paper, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder-based techniques. These techniques require a small number of experimental data points and does not rely on TCAD tools. To demonstrate the effectiveness of our approach, we apply it to a deep neural network-based prediction task for the Ohmic resistance value in Gallium Nitride devices. A 70% reduction in mean absolute error when predicting experimental results is achieved. The inherent flexibility of our approach allows easy adaptation to various tasks, thus making it highly relevant to many applications of the semiconductor industry.
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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors
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Feb 6, 2024 · In design technology co-optimization (DTCO) flows, semiconductor device models are the bridge between the device fabrication and the circuit design, as shown in Fig. 1. Enabling circuit simulations with accurate device models is important for correct analysis of trade-offs of efficiency and accuracy, facilitating optimization of PPAC of circuits.
Jun 1, 2008 · In this review paper we describe a hierarchy of simulation models for modeling state of the art devices. Within the semiclassical simulation arena, emphasis is placed on particle-based device ...
Explore the latest full-text research PDFs, articles, conference papers, preprints and more on DEVICE MODELLING. Find methods information, sources, references or conduct a literature review on ...
Aug 30, 2023 · The semiconductors industry benefits greatly from the integration of machine learning (ML)-based techniques in technology computer-aided design (TCAD) methods. The performance of ML models, however, relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device ...
Apr 29, 2020 · This chapter covers different methods of semiconductor device modeling for electronic circuit simulation. It presents a discussion on physics-based analytical modeling approach to predict device ...
Keywords: device modeling, physics-based model, empirical modeling, TCAD device simulation, SPICE model 1. Introduction Researchers are devoting their time and efforts on the development of efficient and high-speed device models as the requirement for faster, smaller circuits and systems are becoming more and more stringent. These models take ...
In this paper we present the generic device simulator GOOD-SIM, which solves the generalized hydrodynamic equations (HDE's) in semiconductor devices, including non parabolic band structure effects. GOOD-SIM simulates the electrical characteristics of arbitary two-dimensional structures, under user-specified conditions.
May 25, 2021 · The semiconductors industry benefits greatly from the integration of Machine Learning (ML)-based techniques in Technology Computer-Aided Design (TCAD) methods. The performance of ML models however relies heavily on the quality and quantity of training datasets. They can be particularly difficult to obtain in the semiconductor industry due to the complexity and expense of the device fabrication ...
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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 ...