Modeling the faulty behaviour of digital designs using a feed forward neural network approach

Cosmic rays lead to soft errors and faulty behavior in electronic circuits. Knowing about their faulty behavior before fabrication would be helpful. This research proposes an approach for modeling the faulty behaviour of digital circuits. It could be applied in a design flow before circuit fabrication. This is achieved by extracting information about faulty behaviour of circuits from low-level models expressed in the VHDL language. Afterwards the extracted information is used to train high-level artificial neural networks models expressed in C/C++ or MATLAB^TM languages. The trained neural network models are able to replicate the behaviour of circuits in presence of faults. The methodology is based on experiments done with two benchmarks, the ISCAS-C17 and a 4-bit multiplier. Results show that the neural network approach leads to models that are more accurate than a previously reported signature generation method. For the C17, using only 30% of the dataset generated with the LIFTING fault simulator, the neural network is able to replicate the output of the circuit in presence of faults with a mean absolute modeling error below 6%.