容差模拟电路故障模糊诊断方法及其实现

提出了基于SOFM神经网络的容差模拟电路故障模糊诊断方法及其实现。该方法将网络撕裂法和SOFM神经网络相结合进行故障测试．并运用所设计的模糊逻辑神经网络系统判断测试条件，定位容差模拟电路的子网络级故障。仿真试验表明该方法故障定位精确度高。撕裂迅速，有利于大规模容差模拟电路故障诊断的实现。     

Testing of programmable analog neural network chips

Artificial neural network chips can achieve high-speed performance in solving complex computational problems for signal and information processing applications. These chips contain regular circuit units such as synapse matrices that interconnect linear arrays of input and output neurons. The neurons and synapses may be implemented in an analog or digital design style. Although the neural processing has some degree of fault tolerance, a significant percentage of processing defects can result in catastrophic failure of the neural network processors. Systematic testing of these arrays of circuitry is of great importance in order to assure the quality and reliability of VLSI neural network processor chips. The proposed testing method consists of parametric test and behavioral test. Two programmable analog neural chips have been designed and fabricated. The systematic approach used to test the chips is described, and measurement results on parametric test are presented.This research was partially supported by DARPA under Contract MDA 972-90-C-0037 and by National Science Foundation under Grant MIP-8904172.     

基于小波神经网络的模拟电路故障诊断研究

文章提出了一种基于小波神经网络的模拟电路故障诊断方法。这种方法采用正弦信号作为被测电路的输入激励,在时域中对输出信号采样来构造神经网络的训练和测试样本,将自适应学习率及附加动量BP算法训练后的小波神经网络应用于容差模拟电路故障诊断中。仿真试验表明,该方法减少了故障诊断时间和提高了网络的平均诊断正确率。     

Test‐Generation‐Based Fault Detection in Analog VLSI Circuits Using Neural Networks

In this paper, we propose a novel test methodology for the detection of catastrophic and parametric faults present in analog very large scale integration circuits. An automatic test pattern generation algorithm is proposed to generate piece‐wise linear (PWL) stimulus using wavelets and a genetic algorithm. The PWL stimulus generated by the test algorithm is used as a test stimulus to the circuit under test. Faults are injected to the circuit under test and the wavelet coefficients obtained from the output response of the circuit. These coefficients are used to train the neural network for fault detection. The proposed method is validated with two IEEE benchmark circuits, namely, an operational amplifier and a state variable filter. This method gives 100% fault coverage for both catastrophic and parametric faults in these circuits.     

基于BP神经网络的大规模电路模块级故障快速诊断方法

根据大规模电路故障诊断网络撕裂法和交叉撕裂搜索方法,采用基于误差反向传播算法的多层前向神经网络(BP神经网络)记载多次撕裂信息,提出了一种新型基于BP神经网络的大规模电路模块级快速诊断方法。该方法能快速有效地并行处理定位故障模块,具有测前工作量小,实时诊断性强等优点。     

一种基于神经网络的模拟电路故障诊断方法

提出了一种基于模拟电路故障诊断的神经网络方法。这种方法利用小波分解、数据标准化、主成分分析对输入数据进行预处理,采用k个神经元输出的前馈神经网络结构进行有效训练。该方法检测和识别故障准确率高,系统的鲁棒性和稳定性强。     

基于PSpice与LM算法的模拟电路故障诊断方法

采用小波神经网络与Levenberg-Marquardt算法相结合的方法,对模拟电路进行故障诊断;用小波对冲击响应信号进行多尺度分解,进行归一化后,提取故障特征信息作为神经网络的输入而进行分类。将PSpice与Matlab结合不但能有效的诊断模拟电路,且在收敛性和故障准确性上有了大幅提高。实验仿真表明,通过该方法构造的样本集训练出的网络稳定性高于传统方法,适用于神经网络。     

容差模拟电路的软故障诊断的小波方法

给出了容差模拟电路软故障诊断的小波与量子神经网络方法,利用小波分析,取其能反映故障信号特征的成分做为电路故障特征,再输入给量子神经网络,不仅解决了一个可测试点问题,并提高了辨识故障类别的能力,而且在网络训练之前,利用主元分析降低了网络输入维数。实验证明了这种方法的可行性与适用性。     

Fault-based ATPG for linear analog circuits with minimal size multifrequency test sets

An automatic test pattern generation (ATPG) procedure for linear analog circuits is presented in this work. A fault-based multifrequency test approach is considered. The procedure selects a minimal set of test measures and generates the minimal set of frequency tests which guarantee maximum fault coverage and, if required, maximal fault diagnosis, of circuit AC hard/soft faults. The procedure is most suitable for linear time-invariant circuits which present significant frequency-dependent fault effects.For test generation, the approach is applicable once parametric tests have determined DC behaviour. The advantage of this procedure with respect to previous works is that it guarantees a minimal size test set. For fault diagnosis, a fault dictionary containing a signature of the effects of each fault in the frequency domain is used. Fault location and fault identification can be achieved without the need of analog test points, and just in-circuit checkers with an observable go/no-go digital output are required for diagnosis.The procedure is exemplified for the case of an analog biquadratic filter. Three different self-test approaches for this circuit are considered. For each self-test strategy, a set of several test measures is possible. The procedure selects, in each case, the minimal set of test measures and the minimal set of frequency tests which guarantee maximum fault coverage and maximal diagnosis. With this, the self-test approaches are compared in terms of the fault coverage and the fault diagnosability achieved.This work is part of AMATIST ESPRIT-III Basic Research Project, funded by CEC under contract #8820.     

大规模模拟电路故障诊断方法研究

测前工作量大和故障定位过程复杂等问题,使传统的大规模模拟电路故障诊断方法研究不再适应当前的社会发展需求,因此构建高效全面的新型故障诊断方法就变得尤为重要。本文通过对撕裂法和BP网络改进算法等新兴方法进行深入的研究,来得出其具体应用和实施的可能性。     

A Novel Method for the Diagnosis of the Incipient Faults in Analog Circuits Based on LDA and HMM

Diagnosis of incipient faults for electronic systems, especially for analog circuits, is very important, yet very difficult. The methods reported in the literature are only effective on hard faults, i.e., short-circuit or open-circuit of the components. For a soft fault, the fault can only be diagnosed under the occurrence of large variation of component parameters. In this paper, a novel method based on linear discriminant analysis (LDA) and hidden Markov model (HMM) is proposed for the diagnosis of incipient faults in analog circuits. Numerical simulations show that the proposed method can significantly improve the recognition performance. First, to include more fault information, three kinds of original feature vectors, i.e., voltage, autoregression-moving average (ARMA), and wavelet, are extracted from the analog circuits. Subsequently, LDA is used to reduce the dimensions of the original feature vectors and remove their redundancy, and thus, the processed feature vectors are obtained. The LDA is further used to project three kinds of the processed feature vectors together, to obtain the hybrid feature vectors. Finally, the hybrid feature vectors are used to form the observation sequences, which are sent to HMM to accomplish the diagnosis of the incipient faults. The performance of the proposed method is tested, and it indicates that the method has better recognition capability than the popularly used backpropagation (BP) network.     

Defect detection in analog and mixed circuits by neural networks using wavelet analysis

An efficient defect-oriented parametric test method for analog & mixed-signal integrated circuits based on neural network classification of a selected circuit's parameter using wavelet decomposition preprocessing is proposed in this paper. The neural network has been used for detecting catastrophic defects in two experimental analog & mixed-signal CMOS circuits by sensing the abnormalities in selected parameters, observed under defective conditions and by their consequent classification into a proper category. To reduce complexity of the neural network, wavelet decomposition is used to perform preprocessing of the analyzed parameter. Moreover, we show that wavelet analysis brings significant enhancement in the correct classification, and makes the neural network-based test method extremely efficient & versatile for detecting hard-detectable catastrophic defects in analog & mixed-signal circuits.     

A new diagnosis approach for handling tolerance in analog and mixed-signal circuits by using fuzzy math

A novel analysis method for analog circuits test and diagnosis is described in this paper. Diagnosis hypotheses are represented and fuzzy math is used to express the diagnosis hypotheses and strategy. Based on it, new equivalent fault model is presented and used for test node selection and design for test. Especially, parametric fault test for linear analog circuits with tolerance analysis is presented using both sensitivity method and fuzzy analysis method.     

基于小波神经网络和相位差的模拟电路故障诊断

根据模拟电路中存在噪声的问题,提出利用相位差来进行故障诊断。通过正常模式和故障模式下相位差和幅值差的特征提取,建立故障字典。然后利用小波神经网络对故障电路建模,基于该网络学习收敛快,对网络输入不太敏感的特点,实现故障诊断。通过实例证明,该方法不但诊断准确,而且很切合实际模拟电路。     

Testing of RF mixers with adaptive filters

This work describes a technique for testing RF mixers with digital adaptive filters. RF circuits are widely used on data transmission applications, such as wireless communication, radio and portable phone systems. However, traditional analog testing covers mainly linear circuits, being not suitable to non-linear pieces of hardware like analog mixers. Herein, an adaptive non-linear filter is trained so that it can mimic the behavior of a RF mixer. Then, a test stimulus is simultaneously applied to the filter and the mixer and the outputs of both circuits are compared to check whether the circuit under test is faulty or fault free. A prototype of a mixer was built in order to allow fault injection in the circuit under test. Thus, the detection capability of the proposed technique could be checked in a real life circuit. The preliminary results point to a very promising test technique. The test is very precise, low cost and allows a complete fault coverage with a very small testing time.     

基于径向基函数网络的模拟电路故障诊断设计

模拟电路的固有特点使其故障诊断较数字电路困难.相对于BP网络,RBF神经网络具有最佳逼近性能且收敛快、无局部极小,可引入解决上述困难.根据具体电路,定义故障,选定测试点,确定网络结构,用Pspice获得训练样本,经过训练得到RBF网络.网络的输入为从测试点得到的输入向量,输出为对应的故障.为了验证网络的泛化性能,对每种...     

基于加速退化试验的模拟IC寿命评估研究

为解决高可靠性、长寿命模拟集成电路的寿命评估问题,结合半导体器件退化失效的特点,提出了基于加速退化试验的模拟集成电路寿命评估方法。在此基础上,以某型电压基准模拟IC为研究对象,通过对退化数据的分析研究,获得了其在正常工作应力下的寿命数据。     

一种模数混合电路故障诊断新方法

把模拟电路故障诊断的子网络撕裂诊断法与数字电路故障诊断的伪穷举测试法相结合．提出了一种应用于模数混合电路的故障诊断方法。其诊断思想是把串联形式的混合电路，划分成模拟和数字电路两部分．并分别进行诊断。该方法计算量小、诊断定位精度高，适合于工程应用。     

模拟电路故障诊断的结点电压灵敏度比值法

在证明线性电路中结点电压变化量比值等于结点电压灵敏度比值的基础上,提出了结点电压灵敏度比值法,通过结点电压变化量比值和结点电压灵敏度比值的比对确定电路的故障元件。理论分析和实验结果表明,该方法算法简单、诊断速度快,在可测点受限条件下具有较高的诊断精度,特别适合大规模线性模拟电路的故障诊断和测试。     

Soft Fault Diagnosis in Analog Circuits Based on Bispectral Models

Aiming at the problem to locate soft faults in analog circuits, a new approach based on bispectral models is proposed. First, the Volterra kernels of the circuit under test (CUT) are calculated. Then, the Volterra kernels are used to construct bispectral models. By comparison with the fault features of the constructed models, soft faults of linear and weak nonlinear components in the analog circuit are identified and the faults are located. Simulations and experiments show the effectiveness of the proposed method in analog circuits.,