Analog circuit fault diagnosis method based on depth learning and complex characteristics

Abstract

The invention discloses an analog circuit fault diagnosis method based on depth learning and complex characteristics. A fault-free state and all fault states are simulated by using simulation software; different representative working frequency points are set successively; an amplitude and phase of a fault-free signal are measured at each measuring point and a real value and an imaginary value of the signal are obtained by calculation; the real values and the imaginary values are processed to form sample vectors; and tag marking is carried out according to a fault state. A classification network is constructed by using a self-coding network and a classifier; training is carried out by using the sample vectors and the corresponding tags; when a fault diagnosis needs to be carried out on the analog circuit, different representative working frequency points are set successively; current amplitudes and phase are measured at all measuring points; sample vectors are constructed according to a pattern; the sample vectors are inputted into the trained classification network to obtain a classification result, thereby obtaining a fault diagnosis result. According to the analog circuit provided by the invention, on the basis of combination of the self-coding network with complex characteristics of signals, the accuracy of analog circuit fault diagnosis is improved.

Description

technical field [0001] The invention belongs to the technical field of analog circuit fault diagnosis, and more specifically, relates to an analog circuit fault diagnosis method based on deep learning and complex features. Background technique [0002] With the rapid development of integrated circuits, in order to improve product performance, reduce chip area and cost, it is necessary to integrate digital and analog components on the same chip. According to data reports, although the analog part only accounts for 5% of the chip area, its fault diagnosis cost accounts for 95% of the total diagnostic cost. Analog circuit fault diagnosis has always been a "bottleneck" problem in the integrated circuit industry. [0003] At this stage, some relatively well-developed analog circuit fault diagnosis theories have been applied to practice, such as: fault dictionary method in pre-test analog diagnosis method, component parameter identification method and fault verification method in ...

AI Technical Summary

Problems solved by technology

[0005] (1) Since the algorithm is essentially a gradient descent method, and the objective function to be optimized is very complex, there will inevitably be a "sawtooth phenomenon", which makes the neural network algorithm inefficient

[0006] (2) When the problem to be solved is to solve the global extremum of a complex nonlinear function, the algorithm result is likely to fall into a local extremum, resulting in failure of training

[0007] (3) When the algorithm is a multi-layer neural network, each training will produce error diffusion, which will also lead to poor performance of the algorithm

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