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Based on current digital IC fault diagnosis method

Author: BaiJinWei
Tutor: ZhangDeYuan
School: University of Electronic Science and Technology
Course: Detection Technology and Automation
Keywords: Troubleshooting Quiescent Current Dynamic current Wavelet Analysis Neural Network
CLC: TN431.2
Type: Master's thesis
Year: 2008
Downloads: 111
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Abstract


With the development of microelectronics technology , the demand for IC fault diagnosis increasingly urgent . In this paper, an integrated circuit fault detection and location as the main line , the static current detection (IDDQ) and dynamic current detection (IDDT) technology, based on the combination of pattern recognition and classification decision theory, wavelet analysis , the circuit fault diagnosis a more systematic study . Conventional voltage detection method has been relatively mature and has been widely used, but this method is limited fault detection capability . In order to improve fault coverage , fault diagnosis method based on the current being developed in recent years , it is through the supply current signal is extracted from the valid information to achieve fault diagnosis . This paper summarizes and improve existing current detection method based on Matlab and Pspice software through the circuit simulation, verification of this quiescent current detection method and dynamic current detection method for integrated circuits fault diagnosis . In the quiescent current detection : By analyzing the quiescent current of the circuit bridging fault information to achieve fault detection , experimental results show that IDDQ test coverage is still limited , we need a method to IDDQ IDDT effective supplement ; current detection in dynamic ways: through analysis Dynamic peak value of the current waveform to achieve the multi-class fault detection circuit , experimental results confirmed the IDDT method can successfully detect the voltage testing and IDDQ faults can not be detected , but to accurately locate the fault , still using the signal processing methods to extract additional information ; fault diagnosis based on IDDT implementation aspects : the wavelet transform of the circuit in normal mode and fault mode IDDT sampling signal fault feature extraction , and then were using the nearest neighbor method and connection-based pattern recognition method to achieve all kinds of hard and soft faults accurate positioning. Simulation results demonstrate the effectiveness of these two algorithms . In contrast, the connection-based pattern recognition method has higher accuracy and fault resolution. Finally, we use neural network -based fault diagnosis circuit IDDT soft fault on the circuit was simulated experimental results demonstrated BP network troubleshooting capabilities.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Microelectronics, integrated circuit (IC) > Semiconductor integrated circuits ( solid state circuits ) > Bipolar > Digital ICs, logic ICs
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