Abstract: Emerging bio-inspired computing systems simulate the cognitive functions of the brain for the realization of future computing systems. For the development of such efficient neuromorphic electronics, the emulation of short-term and long-term synaptic plasticity behaviors of the biological synapses is an essential step. However, the electronic synaptic devices suffer from higher variability issues which hinder the application of such devices to build neuromorphic systems. For practical applications, it is essential to minimize the cycle-to-cycle and device-to-device variations in the synaptic functions of artificial electronic synapses. This study involves the fabrication of diffusive memristor devices using WTe₂ chalcogenide as the main switching material. The choice of the switching material provides a facile solution to the variability problem. The greater uniformity in the switching characteristics of the WTe₂-based memristor offers higher uniformity for the synaptic emulation. These devices exhibit both volatile and nonvolatile switching properties, allowing them to emulate both short-term and long-term synaptic functions. The WTe₂-based electronic synaptic devices present a high degree of uniformity for the emulation of various essential biological synaptic functions including short-term potentiation (STP), long-term potentiation (LTP), long-term depression (LTD), spike-rate-dependent plasticity (SRDP), and spike-timing-dependent plasticity (STDP). A higher recognition accuracy of ∼92 % is attained for pattern recognition using the modified National Institute of Standards and Technology (MNIST) handwritten digits, which is attributed to the enhanced linearity and higher uniformity of LTP/LTD characteristics.

Key words: Diffusive memristor, Chalcogenide switching layer, WTe₂, Uniformity, Neuromorphic computing