Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques

• URL: http://arxiv.org/abs/2105.11453v2
• Date: Wed, 5 Apr 2023 08:20:32 GMT
• Title: Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques
• Authors: Zeheng Wang, Liang Li, Ross C. C. Leon, Jinlin Yang, Junjie Shi, Timothy van der Laan, and Muhammad Usman
• Abstract summary: We propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder-based techniques. 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.
• Score: 6.170514965470266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Machine Unlearning of Pre-trained Large Language Models [17.40601262379265]
  This study investigates the concept of the right to be forgotten' within the context of large language models (LLMs) We explore machine unlearning as a pivotal solution, with a focus on pre-trained models.
  arXiv  Detail & Related papers  (2024-02-23T07:43:26Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• Qualitative Data Augmentation for Performance Prediction in VLSI circuits [2.1227526213206542]
  This work proposes generating and evaluating artificial data using generative adversarial networks (GANs) for circuit data. The training data is obtained by various simulations in the Cadence Virtuoso, HSPICE, and Microcap design environment with TSMC 180nm and 22nm CMOS technology nodes. The experimental results show that the proposed artificial data generation significantly improves ML models and reduces the percentage error by more than 50% of the original percentage error.
  arXiv  Detail & Related papers  (2023-02-15T10:14:12Z)
• Unifying Synergies between Self-supervised Learning and Dynamic Computation [53.66628188936682]
  We present a novel perspective on the interplay between SSL and DC paradigms. We show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2023-01-22T17:12:58Z)
• An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design [55.62660894625669]
  Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. Recent developments in machine learning have provided alternative solutions in replacing the time-consuming lithography simulations with deep neural networks. We propose a litho-aware data augmentation framework to resolve the dilemma of limited data and improve the machine learning model performance.
  arXiv  Detail & Related papers  (2022-10-27T20:53:39Z)
• Tiny Machine Learning for Concept Drift [8.452237741722726]
  This paper introduces a Tiny Machine Learning for Concept Drift (TML-CD) solution based on deep learning feature extractors and a k-nearest neighbors. The adaptation module continuously updates the knowledge base of TML-CD to deal with concept drift affecting the data-generating process. The porting of TML-CD on three off-the-shelf micro-controller units shows the feasibility of what is proposed in real-world pervasive systems.
  arXiv  Detail & Related papers  (2021-07-30T17:02:04Z)
• Theory-Guided Machine Learning for Process Simulation of Advanced Composites [0.0]
  Theory-Guided Machine Learning (TGML) aims to integrate physical laws into ML algorithms. This paper presents three case studies on thermal management during processing of advanced composites.
  arXiv  Detail & Related papers  (2021-03-30T00:49:40Z)
• Fast IR Drop Estimation with Machine Learning [36.488460476900975]
  Machine learning (ML) techniques have been actively studied for fast IR drop estimation due to their promise and success in many fields. This paper provides a review to the latest progress in ML-based IR drop estimation techniques. It also serves as a vehicle for discussing some general challenges faced by ML applications in electronics design automation (EDA)
  arXiv  Detail & Related papers  (2020-11-26T23:12:37Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.