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.
The first report of a quantized conductance atomic threshold switch (QCATS) using an atomically‐thin hexagonal boron nitride (hBN) layer is provided. This QCATS has applications in memory and logic devices. The QCATS device shows a stable and reproducible conductance quantization state at 1·G0 by forming single‐atom point contact through a monoatomic boron defect in an hBN layer. An atomistic switching mechanism in hBN‐QCATS is confirmed by in situ visualization of mono‐atomic conductive filaments. Atomic defects in hBN are the key factor that affects the switching characteristic. The hBN‐QCATS has excellent switching characteristics such as low operation voltage of 0.3 V, low “off” current of 1 pA, fast switching of 50 ns, and high endurance > 107 cycles. The variability of switching characteristics, which are the major problems of switching device, can be solved by reducing the area and thickness of the switching region to form single‐atom point contact. The switching layer thickness is scaled down to the single‐atom (≈0.33 nm) h‐BN layer, and the switching area is limited to single‐atom defects. By implementing excellent switching characteristics using single‐layer hBN, the possibility of implementing stable and uniform atomic‐switching devices for future memory and logic applications is confirmed.
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...
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