| 基于神经网络的模拟IC测试分类研究 |
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| 引用本文： | 杨建宁,成立.基于神经网络的模拟IC测试分类研究[J].半导体技术,2005,30(3):41-44,40. |
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| 作者姓名： | 杨建宁 成立 |
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| 作者单位： | 江苏大学电气信息工程学院,江苏,镇江,212013 |
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| 摘 要： | 提出了运用神经网络对模拟IC进行芯片合格分类和故障检测的方法.通过BP型神经网络,运用误差反向传播算法,对CMOS运算放大器输入脉冲测试信号,以正常和故障芯片供电电流的时域响应和频域响应作为样本反复训练网络.检测IC故障实验和仿真结果都表明:BP型神经网络可以用来有效、方便地测试模拟IC.
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| 关 键 词： | 模拟IC 人工神经网络 芯片检测 运算放大器 神经网络 测试模拟 分类研究 Artificial Neural Networks Analog Integrated Circuits 仿真结果 实验 故障检测 训练网络 样本 频域响应 时域响应 电流 供电 测试信号 脉冲 输入 算放大器 CMOS 误差反向传播算法 |
| 文章编号： | 1003-353X(2005)03-0041-04 |
Classification of Defective Analog Integrated Circuits Using Artificial Neural Networks |
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| YANG Jian-ning,CHENG Li.Classification of Defective Analog Integrated Circuits Using Artificial Neural Networks[J].Semiconductor Technology,2005,30(3):41-44,40. |
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| Authors: | YANG Jian-ning CHENG Li |
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| Abstract: | A new approach for detecting defects and classification in analog integrated circuits using the feed-forward neural network with the resilient error back-propagation method is presented. The experiment and simulation were performed for a CMOS operational amplifier ICs by adding input impulse test signal. Networks were trained with supply current responses of good and faulty ICs in time domain and frequency domain. Experiments and simulation results show that the BP Neural Network is a very efficient and versatile approach for testing and detection of analog circuits. |
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| Keywords: | analogue IC artificial neural networks detection operational amplifier |
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