Qingjiang Li

Bio: Qingjiang Li is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Memristor & Computer science. The author has an hindex of 12, co-authored 63 publications receiving 555 citations. Previous affiliations of Qingjiang Li include University of Southampton & University of Defence.

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.

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.

A Memristor SPICE Model Accounting for Synaptic Activity Dependence

Abstract: In this work, we propose a new memristor SPICE model that accounts for the typical synaptic characteristics that have been previously demonstrated with practical memristive devices. We show that this model could account for both volatile and non-volatile memristance changes under distinct stimuli. We then demonstrate that our model is capable of supporting typical STDP with simple non-overlapping digital pulse pairs. Finally, we investigate the capability of our model to simulate the activity dependence dynamics of synaptic modification and present simulated results that are in excellent agreement with biological results.

A Ti/AlO x /TaO x /Pt Analog Synapse for Memristive Neural Network

Abstract: Electronic synapse with precise analog weight tuning ability and long-term retention is the vital device foundation of memristor-based neuromorphic computing systems In this letter, we propose a Ti/AlO x /TaO x /Pt memristor as an analog synapse for memristive neural network applications The device shows high uniformity, excellent analog switching behaviors (up to 200 resistance states under triangle pulses) and excellent long-term retention of each state (up to 30 000 s) Furthermore, the precise modulation of the device resistance state (with 17% tolerance) can also be achieved by a finer writing program within 50 cycles

Pulse-induced resistive and capacitive switching in TiO2 thin film devices

Abstract: In this study, we exploit the non-zero crossing current–voltage characteristics exhibited by nanoscale TiO2 based solid-state memristors. We demonstrate that the effective resistance and capacitance of such two terminal devices can be modulated simultaneously by appropriate voltage pulsing. Our results prove that both resistive and capacitive switching arise naturally in nanoscale Pt/TiO2/Pt devices under an external bias, this behaviour being governed by the formation/disruption of conductive filaments through the TiO2 thin film.

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．

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.

Two-dimensional materials for next-generation computing technologies.

Abstract: Rapid digital technology advancement has resulted in a tremendous increase in computing tasks imposing stringent energy efficiency and area efficiency requirements on next-generation computing. To meet the growing data-driven demand, in-memory computing and transistor-based computing have emerged as potent technologies for the implementation of matrix and logic computing. However, to fulfil the future computing requirements new materials are urgently needed to complement the existing Si complementary metal–oxide–semiconductor technology and new technologies must be developed to enable further diversification of electronics and their applications. The abundance and rich variety of electronic properties of two-dimensional materials have endowed them with the potential to enhance computing energy efficiency while enabling continued device downscaling to a feature size below 5 nm. In this Review, from the perspective of matrix and logic computing, we discuss the opportunities, progress and challenges of integrating two-dimensional materials with in-memory computing and transistor-based computing technologies. This Review discusses the recent progress and future prospects of two-dimensional materials for next-generation nanoelectronics.