A Fault Diagnosis Method for Integrated Circuits Based on Information Entropy

Abstract

The invention discloses an integrated circuit fault diagnosis method based on information entropy. The fault diagnosis method uses the characteristic that entropy information is sensitive to the parameters of the tested circuit, uses the Lagrangian multiplier method to derive the probability density function of the output response of the tested circuit, and then uses the maximum likelihood method to estimate the output of the tested circuit. Rényi entropy defines the free parameter α in the formula, and finally uses the probability density function and the free parameter α to calculate the Rényi entropy of the output of the circuit under test, and uses the difference between the Rényi entropy corresponding to the output of the unknown fault circuit and the output of the non-fault circuit, Complete troubleshooting. Compared with the prior art, the invention has good effect in diagnosing parametric faults in noise, has strong robustness, only needs a single measuring point, is applicable to both current signals and voltage signals, and has low computational complexity.

Description

technical field [0001] The invention belongs to the field of integrated circuit testing, in particular to an integrated circuit fault diagnosis method based on information entropy. Background technique [0002] In the industry, the fault diagnosis of integrated circuits has important engineering value and is also a challenging problem, especially for the parametric fault diagnosis of analog integrated circuits. At present, although a large number of electronic systems can be constructed completely using digital technology, analog components and circuits are still basic and indispensable in circuits such as filters, analog-to-digital converters, and phase-locked loops. At the same time, due to the limited measuring points, the lack of typical fault models, the tolerance of components, and the nonlinear effects of components, the fault diagnosis of analog circuits has always been a difficult problem. At present, many research efforts are trying to overcome this problem. [0...

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Problems solved by technology

At the same time, due to the limited measuring points, the lack of typical fault models, the tolerance of components, and the nonlinear effects of components, the fault diagnosis of analog circuits has always been a difficult problem. At present, many research efforts are trying to overcome this problem

[0003] Existing typical methods for fault diagnosis of analog integrated circuits include neural network methods, sensitivity analysis and fuzzy analysis methods. These methods have obvious disadvantages, such as: the time complexity of the algorithm is too high due to the long execution time, and multiple tests are required. node etc.

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