Ru Huang

Bio: Ru Huang is an academic researcher from Peking University. The author has contributed to research in topics: CMOS & MOSFET. The author has an hindex of 33, co-authored 686 publications receiving 4785 citations.

A comprehensive review on emerging artificial neuromorphic devices

Abstract: The rapid development of information technology has led to urgent requirements for high efficiency and ultralow power consumption. In the past few decades, neuromorphic computing has drawn extensive attention due to its promising capability in processing massive data with extremely low power consumption. Here, we offer a comprehensive review on emerging artificial neuromorphic devices and their applications. In light of the inner physical processes, we classify the devices into nine major categories and discuss their respective strengths and weaknesses. We will show that anion/cation migration-based memristive devices, phase change, and spintronic synapses have been quite mature and possess excellent stability as a memory device, yet they still suffer from challenges in weight updating linearity and symmetry. Meanwhile, the recently developed electrolyte-gated synaptic transistors have demonstrated outstanding energy efficiency, linearity, and symmetry, but their stability and scalability still need to be optimized. Other emerging synaptic structures, such as ferroelectric, metal–insulator transition based, photonic, and purely electronic devices also have limitations in some aspects, therefore leading to the need for further developing high-performance synaptic devices. Additional efforts are also demanded to enhance the functionality of artificial neurons while maintaining a relatively low cost in area and power, and it will be of significance to explore the intrinsic neuronal stochasticity in computing and optimize their driving capability, etc. Finally, by looking into the correlations between the operation mechanisms, material systems, device structures, and performance, we provide clues to future material selections, device designs, and integrations for artificial synapses and neurons.

Brain-inspired computing with memristors: Challenges in devices, circuits, and systems

Abstract: This article provides a review of current development and challenges in brain-inspired computing with memristors. We review the mechanisms of various memristive devices that can mimic synaptic and neuronal functionalities and survey the progress of memristive spiking and artificial neural networks. Different architectures are compared, including spiking neural networks, fully connected artificial neural networks, convolutional neural networks, and Hopfield recurrent neural networks. Challenges and strategies for nanoelectronic brain-inspired computing systems, including device variations, training, and testing algorithms, are also discussed.

Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

Abstract: As a key building block of biological cortex, neurons are powerful information processing units and can achieve highly complex nonlinear computations even in individual cells Hardware implementation of artificial neurons with similar capability is of great significance for the construction of intelligent, neuromorphic systems Here, we demonstrate an artificial neuron based on NbOx volatile memristor that not only realizes traditional all-or-nothing, threshold-driven spiking and spatiotemporal integration, but also enables dynamic logic including XOR function that is not linearly separable and multiplicative gain modulation among different dendritic inputs, therefore surpassing neuronal functions described by a simple point neuron model A monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbOx memristor based neurons and nonvolatile TaOx memristor based synapses in a single crossbar array is experimentally demonstrated, showing capability in pattern recognition through online learning using a simplified δ-rule and coincidence detection, which paves the way for bio-inspired intelligent systems Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge Here, the authors demonstrate online learning in a monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbOx memristor neurons and nonvolatile TaOx memristor synapses

Probing memristive switching in nanoionic devices

Abstract: Memristive switching in nanoionic devices involves the interplay between physical, electrochemical and thermochemical processes, which can occur in the bulk or at interfaces. Switching in these devices has been studied using techniques based on imaging and spectroscopy, as well as scanning probe and electrical approaches. The mechanistic insights obtained using these methods have informed the technological development of nanoionic devices over the past few decades, and such knowledge will be key to their further optimization and design. Here we review the different approaches that have been used to examine the underlying processes and dynamics of resistive switching. We evaluate the strengths and weaknesses of these techniques and consider the critical testing conditions and sample requirements needed in these analyses. We show that electron beam and nanotip-based microscopy techniques possess high spatial resolution, which is suited to observing morphological or microstructural properties. However, determining the compositional, valent or local structural attributes demands quantitative, spectroscopic approaches. Based on the respective strengths and weaknesses of the characterization techniques, we propose a general framework for the physical characterization of memristive devices. This Review Article assesses the different techniques used to characterize memristive switching in nanoionic devices and proposes a general framework for such devices, based on the relative strengths and weaknesses in each case.

A native oxide high-κ gate dielectric for two-dimensional electronics

Abstract: Silicon-based transistors are approaching their physical limits and thus new high-mobility semiconductors are sought to replace silicon in the microelectronics industry. Both bulk materials (such as silicon-germanium and III–V semiconductors) and low-dimensional nanomaterials (such as one-dimensional carbon nanotubes and two-dimensional transition metal dichalcogenides) have been explored, but, unlike silicon, which uses silicon dioxide (SiO2) as its gate dielectric, these materials suffer from the absence of a high-quality native oxide as a dielectric counterpart. This can lead to compatibility problems in practical devices. Here, we show that an atomically thin gate dielectric of bismuth selenite (Bi2SeO5) can be conformally formed via layer-by-layer oxidization of an underlying high-mobility two-dimensional semiconductor, Bi2O2Se. Using this native oxide dielectric, high-performance Bi2O2Se field-effect transistors can be created, as well as inverter circuits that exhibit a large voltage gain (as high as 150). The high dielectric constant (~21) of Bi2SeO5 allows its equivalent oxide thickness to be reduced to 0.9 nm while maintaining a gate leakage lower than thermal SiO2. The Bi2SeO5 can also be selectively etched away by a wet chemical method that leaves the mobility of the underlying Bi2O2Se semiconductor almost unchanged. An atomically thin high-κ gate dielectric of Bi2SeO5 can be formed via layer-by-layer oxidization of an underlying two-dimensional semiconductor, allowing high-performance field-effect transistors and inverters to be fabricated.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Memristive devices for computing

Abstract: Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

Metal–Oxide RRAM

Abstract: In this paper, recent progress of binary metal-oxide resistive switching random access memory (RRAM) is reviewed. The physical mechanism, material properties, and electrical characteristics of a variety of binary metal-oxide RRAM are discussed, with a focus on the use of RRAM for nonvolatile memory application. A review of recent development of large-scale RRAM arrays is given. Issues such as uniformity, endurance, retention, multibit operation, and scaling trends are discussed.