Multi-input volistor logic XNOR gates

ABSTRACT

A novel approach utilising the emerging memristor technology is introduced for realising a 2-input primitive XNOR gate. This gate enables in-memory computing and is used as a building block of multi-input XNOR gates. The XNOR gate is realised with eight memristors of two crossbar arrays. The average power consumption of an 8-input XNOR gate is calculated and compared with its counterpart realised with CMOS technology – the XNOR gate consumes less power. ESOP realisation can be directly implemented with XNOR gates. Our simulation results and comparisons show the benefit of the proposed XNOR gate in terms of delay, area, and power.

Volistor logic XNOR gate. (a) Circuit diagram of two-input volistor logic XNOR gate. Input voltages are applied to memristors S ₁ and S ₂ through horizontal wires W _in1 and W _in2, and the output which is logical AND of states S ₁ and S ₂ is calculated by applying V _READ to vertical wire W _XNOR. (b) Block diagram of two-input volistor logic gate. (c) A multi-input volistor logic XNOR gate can be implemented by connecting two XNOR gates though CMOS switches.     

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals,Progress, and Challenges

As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.     

HfO2-Based Memristor as an Artificial Synapse for Neuromorphic Computing with Tri-Layer HfO2/BiFeO3/HfO2 Design

Neuromorphic devices are among the most emerging electronic components to realize artificial neural systems and replace traditional complementary metal–oxide semiconductor devices in recent times. In this work, tri-layer HfO₂/BiFeO₃(BFO)/HfO₂ memristors are designed by inserting traditional ferroelectric BFO layers measuring ≈4 nm after thickness optimization. The novel designed memristor shows excellent resistive switching (RS) performance such as a storage window of 10⁴ and multi-level storage ability. Remarkably, essential synaptic functions can be successfully realized on the basis of the linearity of conductance modulation. The pattern recognition simulation based on neuromorphic network is conducted with 91.2% high recognition accuracy. To explore the RS performance enhancement and artificial synaptic behaviors, conductive filaments (CFs) composed of Hafnium (Hf) single crystal with a hexaganal lattice structure are observed using high-resolution transmission electron microscopy. It is reasonable to believe that the sufficient oxygen vacancies in the inserting BFO thin film play a crucial role in adjusting the morphology and growth of Hf CFs, which leads to the promising synaptic and enhanced RS behavior, thus demonstrating the potential of this memristor for use in neuromorphic computing.     

Adaptive Crystallite Kinetics in Homogenous Bilayer Oxide Memristor for Emulating Diverse Synaptic Plasticity

A critical routine for memristors applied to neuromorphic computing is to approximate synaptic dynamic behaviors as closely as possible. A type of homogenous bilayer memristor with a structure of W/HfO_y/HfO_x/Pt is designed and constructed in this paper. The memristor replicates the structure and oxygen vacancy (V_O) distribution of a complete synapse and its Ca²⁺ distribution, respectively, after the forming process. The detailed characterizations of its atomic structure and phase transformation in and near the conductive channel demonstrate that the crystallite kinetics are adaptively coupled with the V_O migration prompted by directional external bias. The extrusion (injection) of the V_Os and the subsequent crystallite coalescence (separation), phase transformation, and alignment (misalignment) resemble closely the Ca²⁺ flux and neurotransmitter dynamics in chemical synapses. Such adaptation and similarity allow the memristor to emulate diverse synaptic plasticity. This study supplies a kinetic process of conductive channel theory for bilayer memristors. In addition, our memristor has very low energy consumption (5–7.5 fJ per switching for a 0.5 µm diameter device, compatible with a synaptic event) and is therefore suitable for large‐scale integration used in neuromorphic networks.     

All-Optically Controlled Memristor for Optoelectronic Neuromorphic Computing

Neuromorphic computing (NC) is a new generation of artificial intelligence. Memristors are promising candidates for NC owing to the feasibility of their ultrahigh-density 3D integration and their ultralow energy consumption. Compared to traditional electrical memristors, the emerging optoelectronic memristors are more attractive owing to their ability to combine the advantages of both photonics and electronics. However, the inability to reversibly tune the memconductance with light has severely restricted the development of optoelectronic NC. Here, an all-optically controlled (AOC) analog memristor is realized, with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. The device is based on the relatively mature semiconductor material InGaZnO and a memconductance tuning mechanism of light-induced electron trapping and detrapping. It is found that the light-induced multiple memconductance states are nonvolatile. Furthermore, spike-timing-dependent plasticity learning can be mimicked in this AOC memristor, indicating its potential applications in AOC spiking neural networks for highly efficient optoelectronic NC.