Diffusive Memristors with Uniform and Tunable Relaxation Time for Spike Generation in Event-Based Pattern Recognition

A diffusive memristor is a promising building block for brain-inspired computing hardware. However, the randomness in the device relaxation dynamics limits the wide-range adoption of diffusive memristors in large arrays. In this work, the device stack is engineered to achieve a much-improved uniformity in the relaxation time (standard deviation sigma reduced from approximate to 12 to approximate to 0.32 ms). The memristor is further connected with a resistor or a capacitor and the relaxation time is tuned between 1.13 mu s and 1.25 ms, ranging from three orders of magnitude. The hierarchy of time surfaces (HOTS) algorithm, to utilize the tunable and uniform relaxation behavior for spike generation, is implemented. An accuracy of 77.3% is achieved in recognizing moving objects in the neuromorphic MNIST (N-MNIST) dataset. The work paves the way for building emerging neuromorphic computing hardware systems with ultralow power consumption.

Automated summary

This research improves the uniformity of relaxation time in diffusive memristors by engineering the device stack, and achieves tunability in relaxation time. An algorithm utilizing the tunable and uniform relaxation behavior for spike generation is implemented, and achieves high accuracy in object recognition.