Introduction
Flexible neuromorphic electronics for the computing systems of smart wearable electronics have attracted great attention because of their merits in terms of energy efficiency and operating speed \cite{van_de_Burgt_2018,Feng_2019,Kim_2021}. In practical hardware neural networks, a memory device completely mimicking a biological synapse is a crucial component in achieving energy-efficient operations \cite{van_de_Burgt_2018,Lequeux_2016,Sun_2021,Bannur_2022}. An organic material based resistive switching device, i.e., an organic memristor, has been considered as a favorable memory component of flexible neuromorphic systems, in the viewpoints of mechanical flexibility and synaptic functionality \cite{Kim_2021,Park_2019,Lee_2020,Park_2021}. Thus far, diverse mechanisms for the resistive switching of organic memristors have been explored, such as ion migration \cite{Raeis_Hosseini_2018}, ferroelectricity \cite{Lu_2020}, and electrochemical metallization (ECM) \cite{Kim_2021,Lee_2019,Jang_2019,Park_2020}. Among the various types of organic memristors, ECM-based devices have been demonstrated as promising artificial synapses of practical systems owing to their great scalability and superior electrical characteristics \cite{Jang_2019,Park_2020}. For organic ECM memristors, a nanoscale metallic conductive filament (CF) is formed or ruptured in an organic medium under an electric stimulus, resulting in the resistive switching characteristics of such devices. Because the CF growth is mainly governed by the distribution of the electric field in an ECM memristor, the multilevel resistance states can be obtained by controlling the conditions of the electric stimulus \cite{Kim_2021,Jang_2019}. In addition, the stability of the CF is dependent on its structure, and thus, short-term plasticity (STP) and long-term plasticity (LTP) of biological synapses can be mimicked in the ECM device by precisely controlling the CF dynamics \cite{Wang_2016,Hua_2019}. Recently, the development of transient electronics with biodegradable and biocompatible characteristics has been urgently needed as environmental concerns increased \cite{Fu_2016,Gao_2017}. To realize the eco-friendly practical neuromorphic systems with high energy efficiency, it is important to achieve the spike-dependent learning process in the transient artificial synapse. For such operations, the synaptic device with bio-realistic synaptic plasticity is an essential component. Specifically, the artificial synapse should possess the STP and LTP characteristics for training on different time scales of successive electric stimuli through a combination of STP and LTP \cite{Sarwat_2022}. Additionally, for compatibility with other neuromorphic components, the diverse time windows for synaptic plasticity should be obtained in the synapse devices \cite{Lee_2020}. Although several studies on the biomaterial based ECM memristors with transient features have been conducted \cite{Hosseini_2015,He_2016,Wang_2016a,Wu_2016,Sun_2018,Song_2018,Ji_2018,Xu_2018,Lin_2019,Guo_2020,Sueoka_2022}, it is still challenging to achieve bio-realistic synaptic devices with biodegradability and flexibility, due to the difficulties in controlling the CF dynamics.
Poly(vinyl alcohol) (PVA) with high water solubility is a promising polymer for biodegradable films \cite{Dorigato_2011,Ahmed_2020}. It is widely utilized as an insulator in flexible electronics because of its excellent mechanical flexibility and superior electrical characterizations \cite{Zhang_2020,Wang_2022}. Despite such functionalities of PVA for transient and flexible electronics, the ECM phenomenon, essential for the CF growth, has not been reported in the pure PVA medium yet. Previously, PVA has not been regarded as a medium, suitable for the ECM memristor, and it has been used as an insulator for the memristors based on the ion migration \cite{Hmar_2018,Kim_2019,Nguyen_2020} or the dipole alignment \cite{Lei_2014}, and a matrix of an ion-doped electrolyte \cite{Krishnan_2018}. In such devices, only the limited synaptic function (STP or LTP) was demonstrated owing to the inherent characteristics in the switching mechanism (see Table S1). Specifically, for the CF based memristor consisting of the ion-doped PVA matrix, the constant ion density restricted the synaptic function to LTP \cite{Krishnan_2018}. In the memristors based on the ion-doped electrolytes, it is difficult to achieve both the LTP and STP functions because the metal ion density is governed by the doping concentration, not the electric stimuli applied to the devices \cite{Park_2020a,Woo_2021}. For realizing the eco-friendly wearable neural networks with high energy efficiency, it is important to develop the transient ECM memristor with mechanical flexibility and optimized synaptic plasticity for the spike-dependent learning.
In this work, we developed a PVA-based ECM memristor with optimized synaptic plasticity for a transient artificial synapse of an eco-friendly flexible neural network with high energy efficiency, as shown in Figure 1a. The resistive switching effects attributed to the metallic CF formation in the PVA media with different molecular weights (Mw) were analyzed. When the Mw value of the polymer medium is sufficiently low, the resistive switching behaviors based on the ECM phenomenon were effectively achieved, and the memory stability and time windows for synaptic plasticity were found to be effectively tuned by the polymer Mw. On the basis of the understanding of the ECM phenomenon in the polymer medium of PVA, we fabricated a PVA-based flexible memristor with biodegradable characteristics and optimized its synaptic plasticity. The developed memristor exhibited stable nonvolatile memory characteristics during successive mechanical stresses, and it was swiftly dissolved in deionized (DI) water. Moreover, synaptic characteristics for neuromorphic systems including STP, LTP, paired-pulse facilitation (PPF), spike-number-dependent plasticity (SNDP), and spike-rate-dependent plasticity (SRDP) were successfully demonstrated in the developed device. The hardware neural networks based on the device showed the reliable logic operations with high energy efficiency. In the SPICE simulation, the device showed the great potentials for realizing practical artificial neural networks.