An analog non-volatile neural network platform for prototyping RF BIST solutions

We introduce an analog non-volatile neural network chip which serves as an experimentation platform for prototyping custom classifiers for on-chip integration towards fully standalone built-in self-test (BIST) solutions for RF circuits. Our chip consists of a reconfigurable array of synapses and neurons operating below threshold and featuring sub-μW power consumption. The synapse circuits employ dynamic weight storage for fast bidirectional weight updates during training. The learned weights are then copied onto analog floating gate (FG) memory for permanent storage. The chip architecture supports two learning models: a multilayer perceptron and an ontogenic neural network. A benchmark XOR task is first employed to evaluate the overall learning capability of our chip. The BIST-related effectiveness is then evaluated on two case studies: the detection of parametric and catastrophic faults in an LNA and an RF front-end circuits, respectively.