Reconfigurable Multilevel Storage and Neuromorphic Computing Based on Multilayer Phase-Change Memory.

Abstract

In the era of big data, the amount of global data is increasing exponentially, and the storage and processing of massive data put forward higher requirements for memory. To deal with this challenge, high-density memory and neuromorphic computing have been widely investigated. Here, a gradient-doped multilayer phase-change memory with two-level states, four-level states, and linear conductance evolution using different pulse operations is proposed. The mechanism of multilevel states is revealed through high-resolution transmission electron microscopy (HRTEM) and finite-element analysis (FEA), which show that the sequential phase change among different sublayers is realized due to the different physical properties of the sublayers with different doping concentrations. Taking advantage of the devices' linear conductance evolution characteristic, a handwritten digit (28 × 28 pixel) recognition task is implemented with a high learning accuracy of 93.46% by building a simulated artificial neural network made up of this gradient-doped multilayer phase-change memory. It is proved that this gradient-doped multilayer phase-change memory is capable of both binary multilevel digital storage and brain-inspired analog in-memory computing in the same device, enabling reconfigurable applications in the future.