Analog PCB intelligent test system based on neural network

Abstract

The invention discloses a simulating PCB intelligent test system which is based on nerve network, which includes main control PC machine, processor, storage, communication circuit, function signal generator, multi-channel sampling switch matrix, sequential circuit, decoding circuit, A / D converter, sampling retainer, differential amplifier, function signal generator under the control of processor outputs stimulating signal to the stimulating node of the tested circuit, the responding signal of the tested circuit is transmited to the processor by multi-channel sampling circuit, then is transmitted to the main control PC machine by communication circuit, the main control PC machine treats the sampling signal with wavelet packet transform de-noising treatment, and with principal component analysis and normalization processing for obtaining fault feature vector; the fault feature vector is inputted into the trained BP nerve network, the output of the BP nerve network is fault type. The invention can position the PCB test fault to the component level effectively, and improve the scalability of system greatly with the simple and effective test method of CMOS switch array.

Description

technical field [0001] The invention relates to a simulated PCB testing system, in particular to a neural network-based simulated PCB intelligent testing system. Background technique [0002] With the development of large-scale analog integrated circuits, the complexity and density of analog circuits continue to increase, and the failure of any component or device will affect the overall situation. Therefore, stricter requirements are placed on the reliability of analog circuit operation; at the same time, in After the analog circuit fails, it is required to be able to locate the fault in real time for maintenance, debugging and replacement. In essence, analog circuit fault diagnosis is actually equivalent to a classification problem, and it is judged which fault category the circuit state belongs to according to the measurement data. Traditional classification and diagnosis methods require a lot of calculations, especially due to the influence of tolerances, the calculatio...

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Problems solved by technology

However, the BP network uses a search algorithm that descends along the gradient, so it is sensitive to the initial weight vector and can easily converge to a local minimum point

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