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The resistance switching behaviour of several materials has recently attracted considerable attention for its application in non-volatile memory (NVM) devices, popularly described as resistive random access memories (RRAMs). RRAM is a type of NVM that uses a material(s) that changes the resistance when a voltage is applied. Resistive switching phenomena have been observed in many oxides: (i) binary transition metal oxides (TMOs), e.g. TiO(2), Cr(2)O(3), FeO(x) and NiO; (ii) perovskite-type compl

In this Review, memristors are examined from the frameworks of both von Neumann and neuromorphic computing architectures. For the former, a new logic computational process based on the material implication is discussed. It consists of several memristors which play roles of combined logic processor and memory, called stateful logic circuit. In this circuit configuration, the logic process flows primarily along a time dimension, whereas in current von Neumann computers it occurs along a spatial di

Bipolar resistive switching (BRS) as well as unipolar resistive switching (URS) behaviors in thick stacks were investigated. Depending on the current compliance during the electroforming process, either BRS or URS was observed. With a lower current compliance during electroforming, asymmetric current-voltage curves showing BRS were observed in the voltage range to , while with a higher current compliance URS behavior was observed. Furthermore, the permanent transition from BRS to URS was investi

Electroforming effects on the composition, structure, and electrical resistance of Pt/TiO2/Pt switching cells are investigated. The correlation between the electroforming procedure and the resulting bipolar switching behavior is discussed. The dependence of electroforming behavior on atmosphere is also identified, from which we define symmetric or asymmetric electroforming. The symmetry of electroforming is a key factor determining the resulting bipolar switching characteristics. From the experi

Recent progress in deep learning extends the capability of artificial intelligence to various practical tasks, making the deep neural network (DNN) an extremely versatile hypothesis. While such DNN is virtually built on contemporary data centers of the von Neumann architecture, physical (in part) DNN of non-von Neumann architecture, also known as neuromorphic computing, can remarkably improve learning and inference efficiency. Particularly, resistance-based nonvolatile random access memory (NVRA

We overview several efforts to emulate functionalities of basic building blocks, i.e. neurons and synapses, of a mammal’s brain by means of non-biological inorganic systems. These efforts have been put to realize ambitious goals such as the achievement of artificial inorganic brains on silicon wafers, i.e. neuromorphic systems, and neuroprosthetic systems taking part in real brain functionalities by interfacing with real brains. In terms of the keywords, ‘threshold’, ‘analogue’, ‘plasticity’, an

We suggest a possible mechanism for bipolar switching in a $\text{Pt}/{\text{TiO}}_{2}/\text{Pt}$ resistive switching cell in terms of electrochemical reactions involving oxygen ions/vacancies. The electrochemical reactions are considered to take place at an interface between Pt and ${\text{TiO}}_{2}$ solid electrolyte, and they modulate the Schottky barrier height at the interface. Calculation results using this proposed mechanism can explain a bipolar switching behavior and semiquantitatively

Abstract Conventional computing architectures are poor suited to the unique workload demands of deep learning, which has led to a surge in interest in memory-centric computing. Herein, a trilayer (Hf 0.8 Si 0.2 O 2 /Al 2 O 3 /Hf 0.5 Si 0.5 O 2 )-based self-rectifying resistive memory cell (SRMC) that exhibits (i) large selectivity (ca. 10 4 ), (ii) two-bit operation, (iii) low read power (4 and 0.8 nW for low and high resistance states, respectively), (iv) read latency (<10 μs), (v) excellent

Impedance characteristics of 27nm thick anatase TiO2 films showing bistable resistive switching were investigated in the frequency domain (100Hz–10MHz) in various resistance states, a fresh state (before electroforming), a high resistive state (HRS), and a low resistive state (LRS). dc conductance in the film becomes dominent in HRS and LRS and the capacitances in the various states are almost identical. Numerical calculations using finite element analysis were performed for the localized filame

Tunneling-assisted Poole-Frenkel (TAPF) mechanism, which represents electron tunneling from a metal electrode to traps in a nearby insulator layer followed by detrapping of the electrons from the traps by virtue of a lowered potential well due to an applied electric field, is suggested in this study to precisely describe the electrical conduction behavior of a Pt∕HfO2∕Si capacitor. The current density versus the applied electric-field curves of the TAPF conduction show a similar electric-field d

Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed pass

The leakage current characteristics of a 16-nm-thick HfO2 film, grown by atomic-layer-deposition using HfCl4 as Hf precursor and O3 as oxidant, were investigated. The electron injection from the Pt top electrode to the HfO2 films was measured at various temperatures. The measured leakage current versus applied bias voltage curves showed the Poole–Frenkel conduction behavior in the high electric field region. However, the estimated dielectric constant from the Poole–Frenkel fitting corresponds to

The artificial spiking neural network (SNN) is promising and has been brought to the notice of the theoretical neuroscience and neuromorphic engineering research communities. In this light, we propose a new type of artificial spiking neuron based on leaky integrate-and-fire (LIF) behavior. A distinctive feature of the proposed FG-LIF neuron is the use of a floating-gate (FG) integrator rather than a capacitor-based one. The relaxation time of the charge on the FG relies mainly on the tunnel barr

A proof-of-concept relaxation oscillator-based leaky integrate-and-fire (ROLIF) neuron circuit is realized by using an amorphous chalcogenide-based threshold switch and non-ideal operational amplifier (op-amp). The proposed ROLIF neuron offers biologically plausible features such as analog-type encoding, signal amplification, unidirectional synaptic transmission, and Poisson noise. The synaptic transmission between pre- and postsynaptic neurons is achieved through a passive synapse (simple resis