Classification of defective analog integrated circuits using artificial neural networks

This paper presents a new approach for detecting defects in analog integrated circuits using the feedforward neural network trained by the resilient error back-propagation method. A feed-forward neural network has been used for detecting catastrophic faults randomly injected in a simple analog CMOS circuit by classification the differences observed in supply current responses of good and faulty circuit. The experimental classification was performed for time and frequency domain, followed by a comparison of results achieved in both domains. It was shown that neural networks might be very efficient and versatile approach for test of analog circuits since an arbitrary fault class or circuit's parameter can be analyzed. Considered defect types and their successful detection by the neural network; and a possible off-chip hardware implementation of the proposed technique are discussed as well. Moreover, optimized hardware architecture of the selected neural network type was designed using VHDL for FPGA realization.