Accurate CMOS bridge fault modeling with neural network-based VHDL saboteurs

This paper presents a new bridge fault model that is based on a multiple layer feedforward neural network and implemented within the framework of a VHDL saboteur cell. Empirical evidence and experimental results show that it satisfies a prescribed set of bridge fault model criteria better than existing approaches. The new model computes exact bridged node voltages and propagation delay times with due attention to surrounding circuit elements. This is significant since, with the exception of full analog simulation, no other technique attempts to model the delay effects of bridge defects. Yet, compared to these analog simulations, the new approach is orders of magnitude faster and achieves reasonable accuracy; computing bridged node voltages with an average error near 0.006 volts and propagation delay times with an average error near 14 ps.