Integrated circuit fault diagnosis method based on information entropy

Abstract

The invention discloses an integrated circuit fault diagnosis method based on information entropy. The fault diagnosis method comprises steps as follows: the characteristic that entropy information is sensitive to parameters of a to-be-tested circuit is used, a probability density function of output responses of the to-be-tested circuit is derived with the Lagrange multiplier method, then a free parameter alpha in a Renyi entropy definition formula of an output of the to-be-tested circuit is acquired through estimation with a maximum likelihood method, finally, Renyi entropy of the output of the to-be-tested circuit is acquired through computation by the aid of the probability density function and the free parameter alpha, and fault diagnosis is finished by the aid of the difference between Renyi entropy corresponding to an output of the unknown fault circuit and Renyi entropy corresponding to an output of a fault-free circuit. Compared with the prior art, the method has the advantages that the diagnosis effect on parametric faults in noises is good, the robustness is high, only a single test point is required, the method is applicable to current signals and voltage signals, and the computation complexity is low.

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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