Diagnosis of soft faults in analog integrated circuits based on fractional correlation

Abstract

Aiming at the problem of diagnosing soft faults in analog integrated circuits, an approach based on fractional correlation is proposed. First, the Volterra series of the circuit under test (CUT) decomposed by the fractional wavelet packet are used to calculate the fractional correlation functions. Then, the calculated fractional correlation functions are used to form the fault signatures of the CUT. By comparing the fault signatures, the different soft faulty conditions of the CUT are identified and the faults are located. Simulations of benchmark circuits illustrate the proposed method and validate its effectiveness in diagnosing soft faults in analog integrated circuits.

A Network Decomposition Approach of Analog Circuit Based on Alternating Voltage

“…The above experiments show that the proposed method can quickly and accurately identify target sub-network fault conditions. Compared with the literature [1,2,9], The method solves the problem of fault-sensitized dynamic element and improve the scope and efficiency of diagnosis. Compared with the literature [4,5] method, the method eliminate the interaction between the sub-networks, reduce the complexity of the diagnosis, have a small amount of calculation.…”

“…Large-scale analog circuit fault diagnosis is becoming frontier and hot topics in circuit research. In recent years, neural network, wavelet analysis, optimization algorithm, search algorithm and other methods are applied to the diagnosis of large-scale circuits and achieved some results [1][2][3][4][5][6][7], but further detailed and perfect work are still necessary. Overall, the traditional network tear method build circuit equations in tearing point, and determine the fault network by cross-checking the calculated and measured values, which makes poor real-time, high computational, engineering realization difficult.…”