基于神经网络的环振子快速蒙特卡罗分析方法

2018 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2018-04-02 DOI:10.23919/ELINFOCOM.2018.8330633

T. Choi, Hanwool Jeong, Seong-ook Jung

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引用次数: 2

摘要

由于SPICE蒙特卡罗(MC)模拟速度慢，MC样本数量有限，导致环振子统计分析不准确。本文提出了基于人工神经网络的环振子MC仿真方法，其仿真速度比SPICE快5个数量级。结果表明，利用SPICE数据的随机样本进行简单的神经网络训练，可以准确地估计出环形振荡器的优点图。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Fast Monte-Carlo analysis method of ring oscillators with neural networks

Because of the slow speed of SPICE Monte-Carlo (MC) simulation, the limited number of MC samples causes inaccuracy for statistical analysis of ring oscillators. In this paper, we propose the MC simulation method of ring oscillators with artificial neural networks, which shows 5 order faster than SPICE. It is shown that the figure of merits of the ring oscillator can be accurately estimated through simple neural networks training with random samples of SPICE data.

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来源期刊

2018 International Conference on Electronics, Information, and Communication (ICEIC)

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