To implement a SNN using a hardware system, an integrate and fire (I&F) neuron is commonly adopted as a spiking neuron owing to its simplicity. An I&F neuron integrates the input synaptic current and the membrane potential is charged, as shown in Figure 1a. When the membrane potential reaches the threshold voltage of the neuron, the neuron generates spikes to the next synapse layer and resets the membrane potential. Unfortunately, it is becoming burdensome to use conventional CMOS-based neurons in massive neuromorphic hardware due to their large areas and high power consumption. [6] In this regard, volatile thershold switching (TS) devices [7][8][9][10][11] and nonvolatile memory such as resistive random access memory (RRAM) , [12] phase change random access memory (PRAM), [13] ferromagnetic material, [14] and floating body transistor [15] based I&F neurons have been reported to overcome the limitations of conventional CMOS-based neurons. In nonvolatile memory device based I&F neurons wherein the memory device is used for integrating the input synaptic current, an additional circuit is required to return memory device to its initial state in the reset process of the neuron. However, in a TS-based I&F neuron, due to the volatile voltage hysteric switch characteristic of the TS device, a self-reset process is performed without a reset circuit. Thus, it enables the realization of a compact and low power consumption neuron. Although many TS-based I&F neurons have been studied, only the operations of I&F neuron and their biological plausibility have been reported. However, it is necessary to study and understand the correlation between the switching parameter of a TS device and the neuron characteristics for practical application of TS-based I&F neurons in various SNN-based hardware.Therefore, in this work, we investigated the effect of the switching parameters of the TS devices on the characteristics of TS-based I&F neurons through electrical measurements and computational simulation of three different types of neurons using a NbO 2 -based insulator-to-metal transition device (IMT), [16] a B-Te-based ovonic threshold switching (OTS) device, [17] and a Ag/HfO 2 -based atomic-switching TS device. [18] In addition, we confirmed the feasibility of TS-based neuron by simulating SNN, which converted from analog-based ANN prelearned by backpropagation.This study demonstrates an integrate and fire (I&F) neuron using threshold switching (TS) devices to implement spike-based neuromorphic system. An I&F neuron can be realized using the hysteric voltage switch characteristics of a TS device. To investigate the effects of various TS devices on neuron behavior, neurons are compared using three different types of TS device: NbO 2 -based insulator-to-metal transition (IMT) device, B-Te-based ovonic threshold switching device, and Ag/HfO 2 -based atomic-switching TS device. The results show that the off-state resistance and switching time of the TS devices determine the leaky/nonleaky characteristics and types of activation function ...

All solid-state lithium-ion transistors are considered as promising synaptic devices for building artificial neural networks for neuromorphic computing. However, the slow ionic conduction in existing electrolytes hinders the performance of lithium-ion-based synaptic transistors. In this study, we systematically explore the influence of ionic conductivity of electrolytes on the synaptic performance of ionic transistors. Isovalent chalcogenide substitution such as Se in Li3PO4 significantly reduces the activation energy for Li ion migration from 0.35 to 0.253 eV, leading to a fast ionic conduction. This high ionic conductivity allows linear conductance switching in the LiCoO2 channel with several discrete nonvolatile states and good retention for both potentiation and depression steps. Consequently, optimized devices demonstrate the smallest nonlinearity ratio of 0.12 and high on/off ratio of 19. However, Li3PO4 electrolyte (with lower ionic conductivity) shows asymmetric and nonlinear weight-update characteristics. Our findings show that the facilitation of Li ionic conduction in solid-state electrolyte suggests potential application in artificial synapse device development.

mimicking the brain-like functionality are being explored. [2] The development of artificial synapse devices with better synaptic plasticity and high speed is the demand of present technology. Extensive integrated circuits on complementary metal-oxidessemiconductor (CMOS), [3] and two terminal memristors such as phase-change memory (PCM), [4] resistive randomaccess memory (RRAM), [5] and other filament-based memory devices have been attempted for synaptic neural functions, but they lack in symmetricity and linear switching in conductance level; also they consume massive space and energy due to large scale complexity.However, three-terminal electrochemical random-access (ECRAM) may allow for improved multi-level memory storage due to separate read and write paths. Solid-state ECRAM was first demonstrated in 1989 using hydrogen doping of WO 3 , though those failed after 25 cycles and needed high voltage. [6] Recently, ECRAM has been proposed as synaptic devices to build artificial neural networks (ANNs) for neuromorphic computing. [7] An ECRAM, also referred to as "redox transistor or synaptic transistor," consists of a mixed ion-electron conductor channel into which an ion-conducting electrolyte pumps ions under the influence of a gate electrode. [8] The operations of ECRAM are based on modulating the device conductance by ionic doping/de-doping. [6][7]9] In ECRAM, the conventional gate dielectric was substituted by an electrolyte gate, which allows the conduction of various mobile ions, e.g., H + , Li + , and O 2-. The ECRAM synapse has a two-step mechanism: firstly, the synaptic weight update is modulated by the applied gate bias; on the second part, the read is separately operated on a channel at the source-drain terminal. [10] Earlier reported Li + and H + based ECRAM shows recommended synaptic property with high endurance and low energy consumption. [7,11] Li + ion-based ECRAM (Li-ECRAM) has shown very consistent charge-discharge curves and no longer suffers from open circuit potential (OCP) issues. [12] However, Li-ECRAM suffers from a significant shortcoming, such as the devices using nonstandard materials, including lithium transition metal oxides and polymers, all of which cannot be integrated into CMOS circuits for real application. [10b,13] In other respect, proton-based ECRAM (H-ECRAM) devices exhibit a non-zero OCP that is resulting in poor data retention. When the gate is Artificial synapses based on electrochemical random-access memory (ECRAM) have emerged as an important component for neuromorphic chips because they are capable to execute simultaneous signal transmission and memory operations. However, existing ECRAM synapse surfers with compatibility and rapid memory loss issue due to highly reactive Li + and H + cationic species. Here, all-solid-state oxygen ion-based ECRAM (O-ECRAM) synapse, which shows linear weight update characteristics through multi-level nonvolatile analog conductance states is presented. Crucially, an O-ECRAM device delivering the highly stable, nonvolatile multi-leve...

computers due to the dense neural network of synapses. [3,4] Developing a highspeed and a low-cost artificial synapse device to build an artificial neural network to simulate the human brain like functionalities is the dominant scientific goal of the twenty-first century. Colossal exertion has been committed to developing a synapse device that can trigger the synaptic plasticity and nonvolatility. The conventional neural prototype chip was fabricated based on traditional complementary metal-oxide-semiconductor (CMOS) circuits. [5] However, consumption of high power due to a large number of transistors in COMS circuit limits its further development. [6,7] Besides, a different type of two-terminal memristor such as oxidebased resistive random-access memory (RRAM), [8] ferroelectric memory, [9][10][11][12] and phase-change memory (PCM) [13][14][15] has been employed to mimic the synaptic activity due to reversible analog switching. However, reduced control over conductance change and asynchronous read/write operation limit the application of twoterminal devices as a synaptic element. [16] More recently, a nanoionics synaptic transistor (IST) has been extensively studied and proposed as a suitable candidate for artificial synapses. [17][18][19][20] An IST consists of ionically conducting gate electrolyte and insulating or semiconducting channel. An IST relies on field-driven ion migration from an electrolyte to channel, thereby changing its doping state and hence its conductivity. This device shows reversibility near analog switching, nonvolatility, and low switching energy because of a filamentforming free switching. Additionally, these devices give a high level of accuracy to emulate the synaptic functionalities because synaptic weight update modulated by the gate terminal while the read operation performed on a channel.Recently, several reports show the synaptic behavior by adding and extracting the oxygen (O 2− ) or proton (H + ) in a channel from the electrolyte layer. [17,19,21,22] Both oxygen and proton-based synaptic transistors show unstable behavior and non-linearity due to the structural deformation of a channel as well as the electrolyte layer by mobile H + and O 2− ion. The advantage of using Li + ion in IST is due to more excellent Lithium nanoionic transistors have recently emerged as promising artificial synaptic devices for neuromorphic hardware systems. However, mimicking the essential synaptic functionalities including nonvolatile conductance modulation with a near-linear analog weight update has been a crucial milestone in those synaptic devices and has a direct impact on pattern recognition accuracy. The volatile channel conductance change due to the instability of the solid electrolyte interface and lithium-ion nucleation at electrolyte-channel interface are two key phenomena responsible for the nonlinear switching in lithium nanoionics transistor. Graphene is proposed as an atomically thin ionic tunneling layer to establish nonvolatile analog multilevel conduction in lithium nanoionic transisto...

In this study, we introduce a lithium (Li) ion-based three-terminal (3-T) synapse device using WO x as a channel. Our study reveals a key stoichiometry of WO 2.7 for excellent synaptic characteristics that is related to Li-ion diffusivity. The open-lattice structure formed by oxygen deficiency promoted Li-ion injection and diffusion. The optimized stoichiometry and improved Li-ion diffusivity were confirmed by x-ray photoelectron spectroscopy analysis and cyclic voltammetry, respectively. Furthermore, the transient conductance change that inevitably occurs in ion-based synaptic transistors was resolved by applying a two-step voltage pulse scheme. As a result, we achieved a symmetric and linear weight-update characteristic with reduced program/ erase operation time.

arrays, are being actively studied as synaptic devices for use in neuromorphic computing. However, the need for a new type of synaptic device has emerged, owing to write-disturbance issues [6] and the difficulty of obtaining sufficient synaptic properties. [7,8] Among various synaptic memory devices, ion-based synaptic transistors are being studied as promising next-generation solutions based on nearideal synaptic behaviors, low-power operations, reasonable retention, and excellent endurance characteristics.Ion-based synaptic transistors mainly use protons and Li ions as doping elements, which can be easily moved by external biases, owing to their small mass. [9] The electrical conductivity changes linearly according to the concentration of ions injected into the channel [10] and by using it as a synaptic weight ideal training characteristics can be achieved. In the case of a protonbased synaptic transistors, low-voltage operation is possible owing to the small atomic size (ion radius 0.04 Å), and excellent weight-update linearity and endurance characteristics are obtained. [11][12][13][14][15] However, to provide excellent proton-hopping characteristics, polymer materials, such as poly(3,4-ethylenedioxythiophene) polystyrene sulfonate, are used as channels and electrolytes. Hence, it is difficult to utilize conventional complementary metal-oxide-semiconductor (CMOS) technology. [14,15] The structure and operation mechanism of a Li-based synaptic transistor is very similar to that of a Li-ion battery. Li ions are injected or extracted into a channel that acts as a cathode, and its electrical conductivity (i.e., synaptic weight) is determined by the concentration of Li ions in the channel. [10] Li-ion-based synaptic transistors have excellent synaptic properties owing to their small atomic size (ion radius 0.9Å), [16][17][18][19][20] but there are disadvantages, such as Li dendrite generation and open-circuit potential issues. [16,21] Accordingly, on the basis of various prior studies in the RRAM and solid oxide fuel cell fields, research on oxygen ion-based synaptic transistors (OISTs) that can utilize conventional CMOS technology is being actively conducted. [22][23][24][25][26][27][28] The OIST uses transition metal oxide (TMO) as a channel, which can change its electrical conductivity depending on the extraction/injection of oxygen ions. In a recently published

In this study, we investigate a proton-based three-terminal (3-T) synapse device to realize linear weight-update and I-V linearity characteristics for neuromorphic systems. The conductance states of the 3-T synapse device can be controlled by modulating the proton concentration in the WO x channel. Therefore, we estimate the dynamic change of proton concentration in the channel region, which directly affects synaptic behaviors. Our findings indicate that the supply of an excess number of protons from the SiO 2 -H electrolyte and low proton diffusivity in the WO x channel result in asymmetric and non-linear weight-update characteristics. In addition, though the linear I-V characteristics can be obtained using non-stoichiometric WO x , we observe that significant oxygen deficiency in the channel region increases the operating current levels. Thus, based on this information, we introduce optimized conditions of each component in the 3-T synapse device and shape of the gate voltage pulses. As a result, an excellent classification accuracy is achieved using linear weight-update and I-V linearity characteristics under optimized device and pulse conditions.

engineering advancements are speeding up the development of electrolyte materials with lower activation energy barriers and channel materials with lower redox energy barriers. [1a,4] Proton based ECRAM (H-ECRAM) is potentially advantageous because proton (H + ) is a smaller and more rapidly diffusing ion than Li + and O 2− . However, the memory state-retention times of H-ECRAM far remained relatively short with cycling instability. [5] Due to H-ECRAM poor memory state-retention, inference accuracy degrades over time. The poor retention in H-ECRAM is caused by self-discharge of intercalated ions from the channel layer due to existence of non-zero open circuit potential (OCP), which is mainly occurred in polymer-based H-ECRAM. [6] When the gate circuit opens during the read operation, the electrically conductive electrolyte is unable to halt the backflow of electrons, resulting in non-zero OCP and subsequent self-discharge of H + ions. Fuller et al. connected a selector in series with the H-ECRAM gate terminal to address the OCP issue, forming a one-selector-one-H-ECRAM (1S1E) structure that isolates the device and prevents leakage current. [7] In our opinion, the memory state-retention and cycling stability of H-ECRAM can be improved by developing proton-conducting solid electrolytes with electron-blocking properties to lower the self-discharge issue. However, the unavailability of a CMOS-compatible proton-conducting solid electrolyte is the main obstacle. All H-ECRAM presently relied on electrolytes that either cannot be integrated and scale down, such as polymer, [8] ionic liquid, [9] ionic gel, [10] organic material. [5] Herein, atomically thin single-layer hexagonal boron nitride (hBN) is integrated into H-ECRAM as a proton-conducting solid-state electrolyte. Atomically thin 2D material has not yet been exploited in prior research for the purpose of improving memory state-retention and cycling stability of ECRAM devices. Recent research has proven that a few 2D materials exhibit ion transport properties both experimentally and theoretically. [11] Hexagonal boron nitride (hBN) single-layers have been evaluated as a possible material for developing novel ionic transport layers. [12] The honeycomb structure of 2D h-BN is composed of alternating boron and nitrogen atoms. [13] It exhibits superior chemical and thermal stability, as well as mechanical strength. [14] ProtonThe first report on ion transport through atomic sieves of atomically thin 2D material is provided to solve critical limitations of electrochemical randomaccess memory (ECRAM) devices. Conventional ECRAMs have random and localized ion migration paths; as a result, the analog switching efficiency is inadequate to perform in-memory logic operations. Herein ion transport path scaled down to the one-atom-thick (≈0.33 nm) hexagonal boron nitride (hBN), and the ionic transport area is confined to a small pore (≈0.3 nm 2 ) at the single-hexagonal ring. One-atom-thick hBN has ion-permeable pores at the center of each hexagonal ring due to weakened elect...