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首頁(yè)> 外國(guó)專利> Method and apparatus for creating an extraction model using Bayesian inference implemented with the Hybrid Monte Carlo method

Method and apparatus for creating an extraction model using Bayesian inference implemented with the Hybrid Monte Carlo method

機(jī)譯：混合蒙特卡羅方法實(shí)現(xiàn)的利用貝葉斯推理創(chuàng)建提取模型的方法和裝置

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摘要

A system for using machine learning based upon Bayesian inference using a hybrid Monte Carlo method to create a model for performing integrated circuit layout extraction is disclosed. The system of the present invention has two main phases: model creation and model application. The model creation phase comprises creating one or more extraction models using machine-learning techniques. First, a complex extraction problem is decomposed into smaller simpler extraction problems. Then, each smaller extraction problem is then analyzed to identify a set of physical parameters that fully define the smaller extraction problem. Then, for each of the smaller simpler extraction problems, complex mathematical models are created using machine learning techniques. The machine learning is performed by first creating training data sets composed of the identified parameters from typical examples of the smaller extraction problem and the answers to those example extraction problems as solved using a highly accurate physics-based field solver. Next, the system uses Bayesian inference implemented with a hybrid Monte Carlo method to train a set of neural networks for extraction problems. After the creation of a set of models for each of the smaller simpler extraction problems, the machine-learning based models may be used for extraction.
機(jī)譯：公開(kāi)了一種使用基于貝葉斯推斷的機(jī)器學(xué)習(xí)的系統(tǒng)，該貝葉斯推斷使用混合蒙特卡羅方法來(lái)創(chuàng)建用于執(zhí)行集成電路布局提取的模型。本發(fā)明的系統(tǒng)具有兩個(gè)主要階段：模型創(chuàng)建和模型應(yīng)用。模型創(chuàng)建階段包括使用機(jī)器學(xué)習(xí)技術(shù)創(chuàng)建一個(gè)或多個(gè)提取模型。首先，將復(fù)雜的提取問(wèn)題分解為更小的簡(jiǎn)單提取問(wèn)題。然后，然后分析每個(gè)較小的提取問(wèn)題，以識(shí)別一組完全定義較小提取問(wèn)題的物理參數(shù)。然后，對(duì)于每個(gè)較小的較簡(jiǎn)單的提取問(wèn)題，使用機(jī)器學(xué)習(xí)技術(shù)創(chuàng)建復(fù)雜的數(shù)學(xué)模型。通過(guò)首先創(chuàng)建訓(xùn)練數(shù)據(jù)集來(lái)執(zhí)行機(jī)器學(xué)習(xí)，該訓(xùn)練數(shù)據(jù)集由來(lái)自較小提取問(wèn)題的典型示例的識(shí)別參數(shù)組成，以及使用高精度基于物理的場(chǎng)求解器解決的這些示例提取問(wèn)題的答案。接下來(lái)，系統(tǒng)使用通過(guò)混合蒙特卡洛方法實(shí)現(xiàn)的貝葉斯推理來(lái)訓(xùn)練用于提取問(wèn)題的一組神經(jīng)網(wǎng)絡(luò)。在為每個(gè)較小的較簡(jiǎn)單提取問(wèn)題創(chuàng)建了一組模型之后，可以將基于機(jī)器學(xué)習(xí)的模型用于提取。

著錄項(xiàng)

  • 公開(kāi)/公告號(hào)US7103524B1

    專利類型

  • 公開(kāi)/公告日2006-09-05

    原文格式PDF

  • 申請(qǐng)/專利權(quán)人 STEVEN TEIG;ARINDAM CHATTERJEE;

    申請(qǐng)/專利號(hào)US20020062196

  • 發(fā)明設(shè)計(jì)人 STEVEN TEIG;ARINDAM CHATTERJEE;

    申請(qǐng)日2002-01-31

  • 分類號(hào)G06F17/50;G06F9/455;

  • 國(guó)家 US

  • 入庫(kù)時(shí)間 2022-08-21 21:41:27

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