An analog-circuit fault diagnosis method based on continuous wavelet analysis and an ELM network comprises: data acquisition: performing data sampling on output responses of an analog circuit respectively through Multisim simulation to obtain an output response data set; feature extraction: performing continuous wavelet analysis by taking the output response data set of the circuit as training and testing data sets respectively to obtain a wavelet time-frequency coefficient matrix, dividing the coefficient matrix into eight sub-matrixes of the same size, and performing singular value decomposition on the sub-matrixes to calculate a Tsallis entropy for each sub-matrix to form feature vectors of corresponding faults; and fault classification: submitting the feature vector of each sample to the ELM network to implement accurate and quick fault classification. The method of the invention has a better effect on extracting the circuit fault features and can be used to implement circuit fault classification accurately and efficiently.

An analog-circuit fault diagnosis method based on continuous wavelet analysis and an ELM network comprises: data acquisition: performing data sampling on output responses of an analog circuit respectively through Multisim simulation to obtain an output response data set; feature extraction: performing continuous wavelet analysis by taking the output response data set of the circuit as training and testing data sets respectively to obtain a wavelet time-frequency coefficient matrix, dividing the coefficient matrix into eight sub-matrixes of the same size, and performing singular value decomposition on the sub-matrixes to calculate a Tsallis entropy for each sub-matrix to form feature vectors of corresponding faults; and fault classification: submitting the feature vector of each sample to the ELM network to implement accurate and quick fault classification. The method of the invention has a better effect on extracting the circuit fault features and can be used to implement circuit fault classification accurately and efficiently.

The disclosure describes to techniques for detecting field failures or performance degradation of circuits, including integrated circuits (IC), by including additional contacts, i.e. terminals, along with the functional contacts that used for connecting the circuit to a system in which the circuit is a part. These additional contacts may be used to measure dynamic changing electrical characteristics over time e.g. voltage, current, temperature and impedance. These electrical characteristics may be representative of a certain failure mode and may be an indicator for circuit state-of-health (SOH), while the circuit is performing in the field.

The disclosure describes to techniques for detecting field failures or performance degradation of circuits, including integrated circuits (IC), by including additional contacts, i.e. terminals, along with the functional contacts that used for connecting the circuit to a system in which the circuit is a part. These additional contacts may be used to measure dynamic changing electrical characteristics over time e.g. voltage, current, temperature and impedance. These electrical characteristics may be representative of a certain failure mode and may be an indicator for circuit state-of-health (SOH), while the circuit is performing in the field.

The disclosure discloses an analog circuit fault feature extraction method and system based on an optimal wavelet basis function, and belongs to the field of electronic circuit engineering and computer vision, and the method comprises the steps of obtaining output signals of an analog circuit during different faults; sequentially applying wavelet transformation methods based on different wavelet basis functions to extract features of output signals; for each feature, calculating the center position of each fault, the distance from each fault data point to the center position, the farthest position of the fault data point and the average position of the fault data points; and determining an optimal wavelet basis function for analog circuit fault feature extraction according to a score discriminating method.

The disclosure discloses an analog circuit fault feature extraction method and system based on an optimal wavelet basis function, and belongs to the field of electronic circuit engineering and computer vision, and the method comprises the steps of obtaining output signals of an analog circuit during different faults; sequentially applying wavelet transformation methods based on different wavelet basis functions to extract features of output signals; for each feature, calculating the center position of each fault, the distance from each fault data point to the center position, the farthest position of the fault data point and the average position of the fault data points; and determining an optimal wavelet basis function for analog circuit fault feature extraction according to a score discriminating method.

A system may include a trim circuit configured to provide a trim signal to a circuit under test. The trim circuit may be configured to adjust a trim value of the trim signal based on a selection signal and a value signal. The trim signal may cause a key characteristic of the circuit under test to change based on the adjusted trim value. The system may include a production tester configured to determine whether the key characteristic is within a threshold range. Responsive to the key characteristic being within the threshold range, the production tester may stop performing the trim procedure on the circuit under test. Responsive to the key characteristic not being within the threshold range, the production tester may adjust the value signal based on whether the key characteristic is greater than or less than the threshold range.

An analog circuit fault feature extraction method based on a parameter random distribution neighbor embedding winner-take-all method, comprising the following steps: (1) collecting a time-domain response signal of an analog circuit under test, wherein the input of the analog circuit under test is excited by using a pulse signal, a voltage signal is sampled at an output end, and the collected time-domain response signal is an output voltage signal of the analog circuit; (2) applying a discrete wavelet packet transform for the collected time-domain response signal to acquire each wavelet node signal; (3) calculating energy values and kurtosis values of the acquired wavelet node signals to form an initial fault feature data set of the analog circuit; and (4) analyzing the initial fault feature data by the parameter random distribution neighbor embedding winner-take-all method, to acquire optimum low-dimensional feature data. The invention effectively reduces redundancy and interference elements in the fault features, and greatly improves degree of separation of different fault features and degree of polymerization of samples of same fault category.

An analog circuit fault feature extraction method based on a parameter random distribution neighbor embedding winner-take-all method, comprising the following steps: (1) collecting a time-domain response signal of an analog circuit under test, wherein the input of the analog circuit under test is excited by using a pulse signal, a voltage signal is sampled at an output end, and the collected time-domain response signal is an output voltage signal of the analog circuit; (2) applying a discrete wavelet packet transform for the collected time-domain response signal to acquire each wavelet node signal; (3) calculating energy values and kurtosis values of the acquired wavelet node signals to form an initial fault feature data set of the analog circuit; and (4) analyzing the initial fault feature data by the parameter random distribution neighbor embedding winner-take-all method, to acquire optimum low-dimensional feature data. The invention effectively reduces redundancy and interference elements in the fault features, and greatly improves degree of separation of different fault features and degree of polymerization of samples of same fault category.

A method for diagnosing analog circuit fault based on cross wavelet features includes steps of: inputting an excitation signal to an analog circuit under test, and collecting time domain response output signals to form an original data sample set; dividing the original data sample set into a training sample set and a test sample set; performing cross wavelet decomposition on both sets; applying bidirectional two-dimensional linear discriminant analysis to process the wavelet cross spectra of the training sample set and the test sample set, and extracting fault feature vectors of the training sample set and the test sample set; submitting the fault feature vectors of the training sample set to a support vector machine for training an SVM classifier, constructing a support vector machine fault diagnosis model; and inputting the fault feature vectors of the test sample set into the model to perform fault classification.