Time-effective Fault Diagnosis Algorithms for Analog and Mixed-signal Circuits Using Sparsity-aware Multi-class Relevance Vector Machine

Except for the advantages of supporting arbitrary kernels, probabilistic predictions and automatic estimation of hyper-parameters, relevance vector machine (RVM) also encounters some of training time increase and classification accuracy recession, compared with SVM. In order to suppress such ‘nuisance’ imperfections, this paper proposed a sparsity-aware RVM model for multi-class classification (denoted as Sa-MRVM) by developing a configurable singular entropy decision mechanism. Multiple driven data sets captured from both emulational and actual circuits under test (CUTs) are involved to further improve the model's generalization ability and judging confidence. Experimental results carried out on two CUTs indicate that our proposed learning methodology is speedy and accurate enough for real world fault diagnosis tasks of analog and mixed-signal circuits.