Electrolyte-gated transistors (EGTs) have been extensively studied as a next-generation neuromorphic device mimicking the biological ionic flux in synapses. However, its long-term plasticity characteristic lasts only for few seconds because of the rapid self-discharge of electrical double layer. Here, ultraviolet ozone (UVO) treated water-in-bisalt (WiBS)/polymer electrolytegated synaptic transistor (WEST) which excellently implements multiple synaptic functions is proposed. Ultraviolet (UV) light and reactive oxygen radicals generated during UVO treatment form trap sites on the surface of active layer, causing lithium cations in the WiBS/polymer electrolyte to be captured at the electrolyte/active layer interface. The UVO treated WEST shows enhanced nonvolatile memory performance for 10 000 s, up to 1186 times longer than that of the untreated WEST. Also, near-ideal weight update over 10 000 cycle tests with 0.32 and −0.55 nonlinearities of long-term potentiation and depression is acquired with a ten times improved symmetricity. These results confirm that the surface engineering is a key technique for sophisticated ion transport, and demonstrate various applicability of EGTs as a neuromorphic device.
Li + electrolyte-gated transistors (EGTs) have received much attention as artificial synapses for neuromorphic computing. EGTs, however, have been still challenging to achieve long-term synaptic plasticity, which should be linearly and symmetrically controlled with the magnitude of electrical potential at the gate electrode. Herein, a fluoroalkylsilane (FAS) self-assembled monolayer (SAM) is introduced as a channel-electrolyte interlayer with the function of sequential ion-trapping in Li + EGTs. It is demonstrated that the retention of Li + ions can be enhanced, resulting in stable non-volatile channel conductance update with high fidelity, linearity, and symmetry in EGTs treated with FAS with 5 fluoroalkyl chains. Through investigating electrical analysis and chemical analysis, it is verified that fluoroalkyl chains enable the sequential ion-trapping at the channel-electrolyte interface by coulombic attraction between Li + ions and fluorocarbons. Simulations of artificial neural networks using 20 × 20 digits show FAS-treated EGTs are suitable as artificial synapses with an accuracy of 89.71% by identical gate pulses and 91.97% by non-identical gate pulses. A methodological approach is newly introduced for developing synaptic devices based on EGTs for neuromorphic computing with high fidelity.
Li+ electrolyte-gated transistors (EGTs) have attracted significant attention as artificial synapses because of the fast response of Li+ ion, low operating voltage, and applicability to flexible electronics. Due to the inherent nature of Li+ ion, Li+ EGTs show, however, limitations, such as poor long-term synaptic plasticity and nonlinear/nonsymmetric conductance update, which hinder the practical applications of artificial synapses. Herein, Li+ EGTs integrated with poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) ferroelectric polymer as a channel–electrolyte interlayer are presented. Owing to the polarized domains of PVDF-TrFE, the transport of Li+ ions at the channel–electrolyte interface is accelerated, and Li+ ions effectively penetrate the channel. Moreover, the self-diffusion of Li+ ions from the channel to the electrolyte is suppressed by the downward polarized domains. Li+ EGTs, therefore, successfully demonstrate synaptic characteristics, including excitatory postsynaptic current, short-/long-term synaptic plasticity, and paired-pulse facilitation. Also, conductance update in Li+ EGTs shows a dynamic range ( Gmax/ Gmin) of 92.42, high linearity, and distinct stability over 100 cycles. Based on their synaptic characteristics, inference simulations using a convolution neural network for the CIFAR-10 dataset imply that Li+ EGTs are suitable as artificial synapses with an inference accuracy of 89.13%. The new methodological approach addressing modulation of ion dynamics at the interface is introduced for developing practical synaptic devices.
Novel solvent-assisted vacancy engineering (SAVE) is proposed for S vacancy generation in MoS2, considering the solubility and polarity of the solvent. The SAVE-treated MoS2 synaptic memristor shows non-volatile memory characteristics and synaptic behavior.
Synaptic devices that mimic biological neurons have attracted much attention for brain-inspired neuromorphic computing. Especially, synaptic thin-film transistors (TFTs) have emerged with simultaneous signal processing and information storage advantages. However, the analysis of excitatory postsynaptic current (EPSC) relies on an empirical model such as a serial RC circuit, which limits a systematic and in-depth study of synaptic devices in terms of material and electrical properties. Herein, the single-pulse-driven synaptic EPSC (SPSE) model, including capacitive effect and information of the synaptic window, is analytically proposed. The SPSE model can simulate EPSC of synaptic devices at given TFT-operating conditions. EPSC with the SPSE model can be characterized with quantified parameters for the capacitive effects and the synaptic windows, which also depend on the electrical condition applied to TFTs. Various kinds of synaptic-TFTs with different gate insulators (e.g., SiO2 and ion-gel) are used to confirm the performance of the SPSE model. For example, the SPSE model can capture the long-term robustness of ion-gel-based TFTs with specific quantified parameters. In addition, the SPSE model enables the estimation of energy consumption, which can potentially be leveraged to compare the energy cost of EPSC fairly. The SPSE model can provide a guideline to understand the physical properties of synaptic TFTs.
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