Fault Detection in State Variable Filter Circuit Using Kernel Extreme Learning Machine (KELM) Algorithm

Electronic applications have become important in industry, science, and everyday life. Modern applications demand greater complexity and smaller packaging, which makes testing more critical. Testing of analog circuit contributes major cost in IC manufacturing. This paper proposes a new method for fault classification in analog circuits using Extreme Learning Machine (ELM) and Kernel ELM algorithms. ELM is a single hidden layer feed forward neural network (SLFN) which chooses the input weight randomly and computes the output weight analytically. The features of the benchmark circuit are extracted by simulating the transfer function of the circuit. The fault dictionary constructed from the features of the circuit is used as the inputs to the ELM and KELM algorithm. Simulation results show that KELM algorithm has better performance at faster learning speed than the ELM algorithm. KELM algorithm outperforms BP-NN-based and ELM-based approaches significantly with effective classification.