Towards artificial intelligence based automatic adaptive response analyzer for high frequency analog BIST

In this paper we analyze the feasibility of a novel neural networks (NN) -based embedded self-test framework for analog devices and systems. The solution that we propose avoids signal quantization, directly dealing with original analog signals, which enables high-accuracy fault detection through lossless signal processing. This is only possible when the self-test unit is also built using analog components and works accordingly to the principles of analog computer. We use, however, powerful apparatus of discrete-time NN to find parameters of the self-test unit that would resemble the behavior of this NN. We demonstrate the efficiency of our approach using complex non-periodic non-linear analog signal.