In realizing the complex artificial neural networks with high energy efficiency, it is important to achieve the synaptic plasticity analogous to the biological counterparts in an artificial synapse \cite{van_de_Burgt_2018,Bannur_2022,Park_2019} (see Figure 4a). The developed flexible memristor exhibited various synaptic functions under electric stimuli. We first measured excitatory post-synaptic current (EPSC) by applying a 10-μs voltage pulse of 1.5 V as shown in Figure 4b. Under the voltage pulse, the conductance of the device increased and the peak current, EPSC was observed. In addition, the device conductance decreased exponentially after removing the electric stimulus, which is analogous to the STP characteristics of a biological synapse \cite{Salin_1996,Park_2020d}. To further verify the STP functions in the flexible memristor, two successive 1.5-V voltage pulses separated by a time interval of 4 μs were applied to the device (see Figure 4c). The EPSC was larger at the second pulse (A2) than at the first pulse (A1), implying the demonstration of PPF, a STP property, in the device. Typically, for the ECM memristors consisting of an insulating layer without any dopant, the lateral diffusion of the metal atoms, and the resultant self-dissolution of the CF are facilitated in the immature filament, which results in the volatile memory characteristics \cite{Lee_2019,Hua_2019}. When the electric stimulus is not sustained sufficiently to grow the CF stably, the developed device exhibits the short-term memory characteristics. In addition, the short-term memory state of the device can be transited to the long-term memory state by an additional electric stimulus, before the unstable CF has been disrupted completely (see Figure 4d). As shown in Figure S17, the device showed the volatile memory characteristics under the relatively short pulse condition, however, the device was operated as a non-volatile memory at the longer voltage pulse. Figure 4e shows a PPF index estimated as A2/A1, according to a time interval between the applied voltage pulses. As the interval decreased from 8.0 to 0.2 μs, the PPF index increased from 1.01 to 4.61, being an indicative of the stable STP properties of the device \cite{Lao_2021,Zhang_2018}. Furthermore, we confirmed that the time window of synaptic plasticity can be effectively tuned by the PVA Mw, the diffusive parameter for the CF in the device \cite{Lee_2020}, as shown in Figure S18. As Mw increased, the CF stability and PPF index were enhanced, indicating a slower time window for the synaptic plasticity of the PVA based synapse.
We also investigated SRDP and SNDP, the important spike-dependent learning rules in a brain \cite{Park_2019,Lao_2021}, in the flexible device (see Figures 4f and 4g). For SRDP, two consecutive 1.5-V voltage pulses with 10 μs were applied to the device, and the SRDP gain, a ratio of the increased conductance value to the initial conductance, was measured with different time interval conditions for the applied voltage pulses. Decrease in a time interval from 8.0 to 0.2 μs, induced an increase in the SRDP gain from 1.05 to 1188.62. Moreover, the EPSC response and the device conductance were effectively tuned through the voltage pulse number (see Figure 4g). With the pulse number, the EPSC value and the device conductance increased. Note that the non-linear increase in the device conductance is attributed to the abrupt growth of the CF, which is similar to the typical ECM memristors \cite{Jang_2019}.
For mimicking the reliable memory states of biological synapses, we controlled the device conductance by utilizing the varying electric stimuli, as shown in Figure 4h. In the potentiation process, the voltage pulse with 20 μs was gradually increased from 1.10 to 1.58 V. For the depression process, the amplitude of the 20-μs pulse was decreased from −1.00 to −1.48 V. Under such pulse conditions, the device clearly exhibited the multilevel memory states consisting of 16 different conductance levels. It should be noted that, in ECM memristors, voltage pulses with gradually varying amplitudes are ideal electric stimuli for controlling the conductance linearly \cite{Park_2020b}. Table S2 shows the electrical performances and synaptic characteristics of the transient memristors previously reported \cite{Hosseini_2015,He_2016,Wang_2016a,Wu_2016,Sun_2018,Song_2018,Ji_2018,Xu_2018,Lin_2019,Guo_2020,Sueoka_2022}. Although the previous memristors based on various structures exhibited resistive switching characteristics, the synaptic plasticity has not been completely replicated in the devices so far. Based on the optimized synaptic plasticity demonstrated in the developed flexible device, it can be thought that the PVA-based memristor is suitable and ideal for spike-dependent neuromorphic computing applications with high energy efficiency.