In a CuxO solid-electrolyte-based CBRAM cell using an Ag top electrode, electroforming-free and electro-reset processes could be achieved at a specific ex situ annealing temperature of the solid electrolyte.

Corresponding to the principles of biological synapses, an essential prerequisite for hardware neural networks using electronics devices is the continuous regulation of conductance. We implemented artificial synaptic characteristics in a (GeTe/Sb2Te3)16 iPCM with a superlattice structure under optimized identical pulse trains. By atomically controlling the Ge switch in the phase transition that appears in the GeTe/Sb2Te3 superlattice structure, multiple conductance states were implemented by applying the appropriate electrical pulses. Furthermore, we found that the bidirectional switching behavior of a (GeTe/Sb2Te3)16 iPCM can achieve a desired resistance level by using the pulse width. Therefore, we fabricated a Ge2Sb2Te5 PCM and designed a pulse scheme, which was based on the phase transition mechanism, to compare to the (GeTe/Sb2Te3)16 iPCM. We also designed an identical pulse scheme that implements both linear and symmetrical LTP and LTD, based on the iPCM mechanism. As a result, the (GeTe/Sb2Te3)16 iPCM showed relatively excellent synaptic characteristics by implementing a gradual conductance modulation, a nonlinearity value of 0.32, and 40 LTP/LTD conductance states by using identical pulse trains. Our results demonstrate the general applicability of the artificial synaptic device for potential use in neuro-inspired computing and next-generation, non-volatile memory.

Neumann architectures to overcome the von Neumann bottleneck in artificial intelligence applications. [1][2][3][4][5][6][7][8] Neuromorphic architectures, especially spiking neural networks (SNNs), consume considerably less power (≈20 mW), than conventional von Neumann computing architectures (≈100 W). [9] As the main building block of SNNs, spiking neurons, especially complementary-metal-oxidesemiconductor field-effect transistor (C-MOSFET)-based neurons, have been intensively researched. However, the density of such neurons is limited because of the extremely large area of the capacitor (>500 μm 2 per capacitor) required to emulate the integrate function and achieve sufficient capacitance (i.e., several pF per capacitor). [10][11][12][13][14][15][16][17][18] To overcome this problem, the use of capacitor-less spiking neurons has been recommended. Recently, several researchers reported spiking neurons that can exhibit the integrate functionality, based on frameworks such as the phase-change memory, [19,20] resistive-random-access memory, [21][22][23][24][25] conductive-bridge-random-access memory, [26,27] and partially depleted silicon-on-insulator. [28] A spiking neuron principally needs a spike neuron device to emulate the integrate function and a sensing amplifier circuit to generate the fire function in a scheme known as integrate-and-fire. However, the existing studies only empirically presented spiking neuron devices to emulate the integrate function. Certain researchers designed a neuron in software by using a sense amplifier circuit, a controller for operating the neuron, a SNN containing neurons, and synapses, and a pattern recognition test was conducted using software simulations. However, only the realization of a spiking neuron was demonstrated as all the SNN operations based on spiking neuron devices emulating the integrate function were conducted via simulations.This study represents the first attempt at developing a conductive-bridge neuron emulating an integrate-and-fire function as an alternative to conventional C-MOSFET-based spiking neurons. The neuron was composed of a conductive-bridge-neuron device, sensing amplifier, and latch circuit. The conductivebridge-neuron device was fabricated in a simple manner by adopting a vertical device structure including a CuTe top electrode, TiO 2 resistive layer, and TiN bottom electrode. The Cu atoms diffused from the CuTe top electrode could easily form Cu filaments in the TiO 2 resistive layer. This phenomenon is of significance because Cu filaments in the TiO 2 resistiveThe neuronal density of complementary metal-oxide-semiconductor field-effect transistor-based neurons is limited because of the use of capacitors. Therefore, a novel neuron is fabricated using a conductive-bridge-neuron device, currentmirror-type sense amplifier, latch, micro-controller-unit, and digital-analogconverters. This neuron exhibits a typical integrate-and-fire function; in particular, the generation frequency of the fire spikes at the neuron exponentially increases with the i...

Recent research on artificial intelligence (AI) has focused on the computational performance of the human brain as a model to process large amounts of data to overcome the limits of current technology. [1,2] This is because the conventional von-Neumann architecture used to operate current AI algorithms causes a performance bottleneck between the computation required and the capacity of memory units. To achieve performance comparable to the biological brain using electronic devices, neuromorphic computing systems have been proposed. [3] This design, consisting of numerous artificial synapses that are essential for hardware advancement, has recently been demonstrated to have power-efficient computation and be extremely compact. [4] Therefore, the artificial synapse should be a simple twoterminal device to achieve brain-level (%10 15 synapses) compactness. Notably, artificial synaptic devices have low energy consumption for gradual conductance states to realize analog-like transitions, including long-term potentiation/longterm depression (LTP/LTD) and spike-timing-dependent plasticity. [5] Based on emerging nonvolatile memory technologies for realizing such characteristics, the devices are classified into phase-change synaptic devices, [6] resistive change synaptic devices, [7] and conductive-bridge synaptic devices relying on a physical switching mechanism. [8] Among them, phase-change synaptic devices have attracted attention because of their reliability and scalability down to the nanometer regime. [6,9] Ge 2 Sb 2 Te 5 alloys, a commonly used phase-change material, exhibit unique switching that creates a resistance difference between amorphous (RESET) and crystalline (SET) states by joule heating. However, a large electrical programming current pulse is needed for melting phase-change materials. Although several efforts have been made to reduce the RESET current, more energyefficient structures or materials are still needed, including stability improvements such as atomic migration, resistance drift, and phase segregation. [10] For this reason, an interfacial phase-change memory (iPCM) with a superlattice-like structure created by alternately depositing a GeTe thin film and Sb 2 Te 3 thin film was introduced by Tominaga et al. [11] The resistance difference is created by the behavior of Ge atoms in the GeTe film sandwiched by the van der Waals gap between the Sb 2 Te 3 films. Although the switching mechanism of iPCM has still not been elucidated, it operates at low energy consumption by restricting the atomic movement of Ge atoms. [12][13][14] Many approaches have been used to improve the properties of memory devices of the iPCM; however, the experimental configuration of an artificial synapse still needs to be investigated. In this article, we demonstrate the synaptic properties by fabricating different numbers of GeTe/Sb 2 Te 3 layers via sputtering.