Hui Xu

Bio: Hui Xu is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Memristor & Neuromorphic engineering. The author has an hindex of 14, co-authored 97 publications receiving 760 citations.

Eliminating Negative‐SET Behavior by Suppressing Nanofilament Overgrowth in Cation‐Based Memory

Abstract: Negative-SET behavior is observed in various cation-based memories, which degrades the device reliability. Transmission electron microscopy results demonstrate the behavior is caused by the overgrowth of the conductive filament (CF) into the Pt electrode. The CF overgrowth phenomenon is suppressed and the negative-SET behavior is eliminated by inserting an impermeable graphene layer. The graphene-based devices show high reliability and satisfying performance.

Design of Ternary Neural Network With 3-D Vertical RRAM Array

Abstract: Recently, 2-D cross-point array of resistive random access memory (RRAM) has been proposed for implementing the weighted sum and weight update operations to accelerate the neuro-inspired learning algorithms on chip. This paper aims to extend such 2-D cross-point array to 3-D vertical array for storing and computing the large-scale weight matrices in the neural network. Considering the fabrication and 3-D integration of analog synapses (i.e., multilevel RRAM devices) are premature at this stage, we propose using today’s available digital or binary RRAM devices for implementing a ternary neural network, which aggressively reduces the weight precision to ternary levels (+1, 0,−1) for the weighted sum in both feedforward and backward inference, while the multiple 3-D layers could serve for accumulating the small errors in a higher precision format for weight update. Compared to the 2-D implementation, the proposed 3-D vertical implementation shows larger read/write margin for weighted sum/weight update, smaller latency, and energy consumption for weight update. This paper demonstrates the attractiveness for building a monolithic 3-D neuromorphic hardware platform.

Short-Term and Long-Term Plasticity Mimicked in Low-Voltage Ag/GeSe/TiN Electronic Synapse

Abstract: The electronic synapse, which can vividly emulate short-term and long-term plasticity, as well as voltage sensitivity, in the bio-synapse, is the vital device foundation for brain-inspired neuromorphic computing. In this letter, we propose a Ag/GeSe/TiN memristor as an electronic synapse for brain-inspired neuromorphic applications. Due to the electromigration and diffusion of Ag cation, the volatile and non-volatile switching behaviours are coexistent in this device. Various synaptic functions, including short-term plasticity, long-term plasticity, pair-pulse facilitation, and spike timing-dependent plasticity, have been successfully eliminated in Ag/GeSe/TiN devices. Furthermore, all the synaptic functions are induced by the spiking stimuli with amplitudes of several hundred millivolts. All the results demonstrate that the Ag/GeSe/TiN device has great potential for brain-inspired computing systems in the future.

Comparison of total variation algorithms for electrical impedance tomography.

Abstract: The applications of total variation (TV) algorithms for electrical impedance tomography (EIT) have been investigated. The use of the TV regularisation technique helps to preserve discontinuities in reconstruction, such as the boundaries of perturbations and sharp changes in conductivity, which are unintentionally smoothed by traditional norm regularisation. However, the non-differentiability of TV regularisation has led to the use of different algorithms. Recent advances in TV algorithms such as the primal dual interior point method (PDIPM), the linearised alternating direction method of multipliers (LADMM) and the spilt Bregman (SB) method have all been demonstrated successful EIT applications, but no direct comparison of the techniques has been made. Their noise performance, spatial resolution and convergence rate applied to time difference EIT were studied in simulations on 2D cylindrical meshes with different noise levels, 2D cylindrical tank and 3D anatomically head-shaped phantoms containing vegetable material with complex conductivity. LADMM had the fastest calculation speed but worst resolution due to the exclusion of the second-derivative; PDIPM reconstructed the sharpest change in conductivity but with lower contrast than SB; SB had a faster convergence rate than PDIPM and the lowest image errors.

Joint Blind Calibration for Mixed Mismatches in Two-Channel Time-Interleaved ADCs

Abstract: To further enhance the dynamic performance of time-interleaved analog-to-digital converters (TI-ADCs), both linear and nonlinear mismatches should be estimated and compensated for. This paper introduces a method for joint calibration of several types of linear and nonlinear mismatch errors in two-channel TI-ADCs. To demonstrate the generality of this method, we take different scenarios into account, including static and dynamic mixed mismatch models. The proposed method utilizes a normalized least-mean square (N-LMS) algorithm as well as a certain low degree of oversampling for the overall converter to estimate and compensate for the mixed mismatch errors. The calibration performance and computational complexity are investigated and evaluated through simulations.

Neuroscience 細胞死：最近の知見

Abstract: 中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Neuro-Inspired Computing With Emerging Nonvolatile Memorys

Abstract: This comprehensive review summarizes state of the art, challenges, and prospects of the neuro-inspired computing with emerging nonvolatile memory devices. First, we discuss the demand for developing neuro-inspired architecture beyond today’s von-Neumann architecture. Second, we summarize the various approaches to designing the neuromorphic hardware (digital versus analog, spiking versus nonspiking, online training versus offline training) and discuss why emerging nonvolatile memory is attractive for implementing the synapses in the neural network. Then, we discuss the desired device characteristics of the synaptic devices (e.g., multilevel states, weight update nonlinearity/asymmetry, variation/noise), and survey a few representative material systems and device prototypes reported in the literature that show the analog conductance tuning. These candidates include phase change memory, resistive memory, ferroelectric memory, floating-gate transistors, etc. Next, we introduce the crossbar array architecture to accelerate the weighted sum and weight update operations that are commonly used in the neuro-inspired machine learning algorithms, and review the recent progresses of array-level experimental demonstrations for pattern recognition tasks. In addition, we discuss the peripheral neuron circuit design issues and present a device-circuit-algorithm codesign methodology to evaluate the impact of nonideal device effects on the system-level performance (e.g., learning accuracy). Finally, we give an outlook on the customization of the learning algorithms for efficient hardware implementation.

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Abstract: Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

Designing crystallization in phase-change materials for universal memory and neuro-inspired computing

Abstract: The global demand for data storage and processing has increased exponentially in recent decades. To respond to this demand, research efforts have been devoted to the development of non-volatile memory and neuro-inspired computing technologies. Chalcogenide phase-change materials (PCMs) are leading candidates for such applications, and they have become technologically mature with recently released competitive products. In this Review, we focus on the mechanisms of the crystallization dynamics of PCMs by discussing structural and kinetic experiments, as well as ab initio atomistic modelling and materials design. Based on the knowledge at the atomistic level, we depict routes to improve the parameters of phase-change devices for universal memory. Moreover, we discuss the role of crystallization in enabling neuro-inspired computing using PCMs. Finally, we present an outlook for future opportunities of PCMs, including all-photonic memories and processors, flexible displays with nanopixel resolution and nanoscale switches and controllers. Chalcogenide phase-change materials (PCMs) are leading candidates for non-volatile memory and neuro-inspired computing devices. This Review focuses on the crystallization mechanisms of PCMs as well as the design principles to achieve PCMs with high switching speeds and good data retention, which will aid the development of PCM-based universal memory and neuro-inspired devices.

Robust memristors based on layered two-dimensional materials

Abstract: Van der Waals heterostructure based on layered two-dimensional (2D) materials offers unprecedented opportunities to create materials with atomic precision by design. By combining superior properties of each component, such heterostructure also provides possible solutions to address various challenges of the electronic devices, especially those with vertical multilayered structures. Here, we report the realization of robust memristors for the first time based on van der Waals heterostructure of fully layered 2D materials (graphene/MoS2-xOx/graphene) and demonstrate a good thermal stability lacking in traditional memristors. Such devices have shown excellent switching performance with endurance up to 107 and a record-high operating temperature up to 340oC. By combining in situ high-resolution TEM and STEM studies, we have shown that the MoS2-xOx switching layer, together with the graphene electrodes and their atomically sharp interfaces, are responsible for the observed thermal stability at elevated temperatures. A well-defined conduction channel and a switching mechanism based on the migration of oxygen ions were also revealed. In addition, the fully layered 2D materials offer a good mechanical flexibility for flexible electronic applications, manifested by our experimental demonstration of a good endurance against over 1000 bending cycles. Our results showcase a general and encouraging pathway toward engineering desired device properties by using 2D van der Waals heterostructures.