Efficient Resistive Switching and Spike Rate Dependent Plasticity in a New CuCrO₂ Memristor for Plausible Neuromorphic Systems

Abstract

In this work, we introduce a new class of p-type transparent conductive oxide (TCO) CuCrO 2 (150 nm) heterogeneously integrated onto FTO/glass for forming free memristor based neuromorphic applications. The fabricated Al/CuCrO 2 /FTO memristors demonstrate a reliable bipolar resistive switching with an ON/OFF ratio of 1000. The retention of the device was found to be steady even beyond 10 6 s, which demonstrates its non-volatility. The current-voltage (I-V) characteristics were fitted to evaluate its transport properties and a band-diagram was projected to have a better insight of the device operational principles. To validate the experimental observations, a new model has been developed, and the simulated I-V behavior was analogues to the experimental one. Efforts were then devoted to observe long-term potentiation (LTP) and longterm depression (LTD) utilizing identical but opposite pulses to evaluate the device's efficacy for synaptic applications. The synaptic behavior was well controlled by the pulse (pulse amplitude and width) variations. The conductance change was found to be symmetric and then saturated, which reflects the popular biological Hebbian rules. Finally, a long-term synaptic modulation has been implemented by establishing the spike rate dependent plasticity (SRDP) rule, which is a part of spiking neural networks and advantageous to mimic the brain's capability at low power. All the obtained experimental results were systematically corroborated by neural network simulation. Overall, our approach provides a new road map towards the development of TCO based alternative memristors, which can be employed to mimic the synaptic plasticity for energy-efficient bioinspired neuromorphic systems and non-Von Neumann computer architectures.

Realization of Multiple Synapse Plasticity by Coexistence of Volatile and Nonvolatile Characteristics of Interface Type Memristor

“…One of the frequently trained Hebbian learning rules is SRDP, in which the term “rate” refers to the frequency of the spike-firing activity. When the spike rate is high, the synaptic connection between neurons and synapses is strengthened, thus increasing output response . Ten sequential pulses were applied to the Pt/WO x /ITO device for observing SRDP.…”

“…When the spike rate is high, the synaptic connection between neurons and synapses is strengthened, thus increasing output response. 62 Ten sequential pulses were applied to the Pt/WO x /ITO device for observing SRDP. The pulse amplitude and width were fixed to 3.2 V and 4 ms, respectively, and the intervals were varied (five different test conditions) to represent different spike rates.…”

Research Progress of Neural Synapses Based on Memristors

“…Li et al designed a synapse based on a sulfur memristor, which can achieve the STDP rule, as shown in Figure 7 [49]. In addition, the STDP rule has been observed in memristors based on CuCrO 2 [50], Al 2 O 3 /TiO 2−x [51], WO 3 [52], and TaO x [53]. However, the non-overlapping method can be used for the second-order memristor to implement the STDP rule.…”

“…And it can be maintained for 1000 s, exhibiting LTP and LTD characteristics. The conductivity change rate increases as the difference in input frequency between presynaptic and postsynaptic increases [50]. In addition, changes in synaptic weight depend on historical states in the SRDP rule.…”

Recent progress in transparent memristors

“…The development of these multifunctional memristors makes it possible to function as neurons and synapses from a single device. Today, the majority of neuromorphic memristors are driven by electrical signals [27,28]. Compared to electrical signals, light has ultrahigh speed, wide bandwidth, low crosstalk, and other advantages.…”