In-depth analysis on electrical parameters of floating gate IGZO synaptic transistor affecting pattern recognition accuracy

Reliable synaptic plasticity of InGaZnO transistor with TiO2interlayer

We demonstrate an InGaZnO (IGZO)-based synaptic transistor with a TiO2buffer layer. The structure of the synaptic transistor with TiO2inserted between the Ti metal electrode and an IGZO semiconductor channel O2trapping layer produces a large hysteresis window, which is crucial for achieving synaptic functionality. The Ti/TiO2/IGZO synaptic transistor exhibits reliable synaptic plasticity features such as excitatory post-synaptic current, paired-pulse facilitation, and potentiation and depression, originating from the reversible charge trapping and detrapping in the TiO2layer. Finally, the pattern recognition accuracy of Modified National Institute of Standards and Technology handwritten digit images was modeled using CrossSim simulation software. The simulation results present a high image recognition accuracy of ∼89%. Therefore, this simple approach using an oxide buffer layer can aid the implementation of high-performance synaptic devices for neuromorphic computing systems.

Indium-Gallium-Zinc Oxide-Based Synaptic Charge Trap Flash for Spiking Neural Network-Restricted Boltzmann Machine

Recently, neuromorphic computing has been proposed to overcome the drawbacks of the current von Neumann computing architecture. Especially, spiking neural network (SNN) has received significant attention due to its ability to mimic the spike-driven behavior of biological neurons and synapses, potentially leading to low-power consumption and other advantages. In this work, we designed the indium-gallium-zinc oxide (IGZO) channel charge-trap flash (CTF) synaptic device based on a HfO2/Al2O3/Si3N4/Al2O3 layer. Our IGZO-based CTF device exhibits synaptic functions with 128 levels of synaptic weight states and spike-timing-dependent plasticity. The SNN-restricted Boltzmann machine was used to simulate the fabricated CTF device to evaluate the efficiency for the SNN system, achieving the high pattern-recognition accuracy of 83.9%. We believe that our results show the suitability of the fabricated IGZO CTF device as a synaptic device for neuromorphic computing.

All-Solid-State Synaptic Transistors with Lithium-Ion-Based Electrolytes for Linear Weight Mapping and Update in Neuromorphic Computing Systems

Neuromorphic computing, an innovative technology inspired by the human brain, has attracted increasing attention as a promising technology for the development of artificial intelligence systems. This study proposes synaptic transistors with a Li1-xAlxTi2-x(PO4)3 (LATP) layer to analyze the conductance modulation linearity, which is essential for weight mapping and updating during on-chip learning processes. The high ionic conductivity of the LATP electrolyte provides a large hysteresis window and enables linear weight update in synaptic devices. The results demonstrate that optimizing the LATP layer thickness improves the conductance modulation and linearity of synaptic transistors during potentiation and degradation. A 20 nm-thick LATP layer results in the most nonlinear depression (αd = -6.59), whereas a 100 nm-thick LATP layer results in the smallest nonlinearity (αd = -2.22). Additionally, a device with the optimal 100 nm-thick LATP layer exhibits the highest average recognition accuracy of 94.8% and the smallest fluctuation, indicating that the linearity characteristics of a device play a crucial role in weight update during learning and can significantly affect the recognition accuracy.

A multilevel electrolyte-gated artificial synapse based on ruthenium-doped cobalt ferrite

Synaptic devices that emulate synchronized memory and processing are considered the core components of neuromorphic computing systems for the low-power implementation of artificial intelligence. In this regard, electrolyte-gated transistors (EGTs) have gained much scientific attention, having a similar working mechanism as the biological synapses. Moreover, compared to a traditional solid-state gate dielectric, the liquid dielectric has the key advantage of inducing extremely large modulation of carrier density while overcoming the problem of electric pinholes, that typically occurs when using large-area films gated through ultra-thin solid dielectrics. Herein we demonstrate a three-terminal synaptic transistor based on ruthenium-doped cobalt ferrite (CRFO) thin films by electrolyte gating. In the CRFO-based EGT, we have obtained multilevel non-volatile conductance states for analog computing and high-density storage. Furthermore, the proposed synaptic transistor exhibited essential synaptic behavior, including spike amplitude-dependent plasticity, spike duration-dependent plasticity, long-term potentiation, and long-term depression successfully by applying electrical pulses. This study can motivate the development of advanced neuromorphic devices that leverage simultaneous modulation of electrical and magnetic properties in the same device and show a new direction to synaptic electronics.

Highly Reproducible Heterosynaptic Plasticity Enabled by MoS2/ZrO2-x Heterostructure Memtransistor

Electrically tunable resistive switching of a polycrystalline MoS₂-based memtransistor has attracted a great deal of attention as an essential synaptic component of neuromorphic circuitry because its switching characteristics from the field-induced migration of sulfur defects in the MoS₂ grain boundaries can realize multilevel conductance tunability and heterosynaptic functionality. However, reproducible switching properties in the memtransistor are usually disturbed by the considerable difficulty in controlling the concentration and distribution of the intrinsically existing sulfur defects. Herein, we demonstrate reliable heterosynaptic characteristics using a memtransistor device with a MoS₂/ZrO_2-x heterostructure. Compared to the control device with the MoS₂ semiconducting channel, the Schottky barrier height was more effectively modulated by the insertion of the insulating ZrO_2-x layer below the MoS₂, confirmed by an ultraviolet photoelectron spectroscopy analysis and the corresponding energy-band structures. The MoS₂/ZrO_2-x memtransistor accomplishes dual-terminal (drain and gate electrode) stimulated multilevel conductance owing to the tunable resistive switching behavior under varying gate voltages. Furthermore, the memtransistor exhibits long-term potentiation/depression endurance cycling over 7000 pulses and stable pulse cycling behavior by the pulse stimulus from different terminal regions. The promising candidate as an essential synaptic component of the MoS₂/ZrO_2-x memtransistors for neuromorphic systems results from the high recognition accuracy (∼92%) of the deep neural network simulation test, based on the training and inference of handwritten numbers (0-9). The simple memtransistor structure facilitates the implementation of complex neural circuitry.