HfZrOx-based capacitive synapses with highly linear and symmetric multilevel characteristics for neuromorphic computing

Abstract

A hardware based artificial neural network (ANN), which holds the potential to alleviate the computation load and energy of a digital computer, has propelled the development of memory devices that can resemble the synapse. Memcapacitors, especially based on ferroelectric materials, with theoretically no static power, nondestructive readout, and multiple polarization states, are expected to have good energy efficiency and endurance as emerging artificial synapses. However, conventional ferroelectric devices are characterized with extremely high remnant polarization, which requires high energy for polarization state updating and always leads to low linearity and symmetry in updating properties. Here, we show a memcapacitive synapse based on an Au/HfZrOx (HZO)/Au ferroelectric memcapacitor with moderate remnant polarization that can offer unexceptionable updating properties for building an ANN. The memcapacitor demonstrates more than 64 weight states with an ultralow weight updating energy of ≤3.0 fJ/μm2. Both potentiation and depression synaptic characteristics show an ultralow non-linearity of <10−2. Based on these properties, a two-layer restricted Boltzmann machine is built based on this memcapacitive synapse, and it can be trained to reconstruct incomplete images. The reconstructed images show reduced Euclidean distance to originals in comparison with that of the incomplete images. Furthermore, the memcapacitive synapse is also tested by a handwritten digits recognition task based on a simple perceptron, and the pattern recognition accuracy is as high as 93.4%. These results indicate that the HZO-based capacitive synapse devices have great potential for future high-efficiency neuromorphic systems.