RRAM-based synapse devices for neuromorphic systems

Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system

Conventional imaging and recognition systems require an extensive amount of data storage, pre-processing, and chip-to-chip communications as well as aberration-proof light focusing with multiple lenses for recognizing an object from massive optical inputs. This is because separate chips (i.e., flat image sensor array, memory device, and CPU) in conjunction with complicated optics should capture, store, and process massive image information independently. In contrast, human vision employs a highly efficient imaging and recognition process. Here, inspired by the human visual recognition system, we present a novel imaging device for efficient image acquisition and data pre-processing by conferring the neuromorphic data processing function on a curved image sensor array. The curved neuromorphic image sensor array is based on a heterostructure of MoS₂ and poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane). The curved neuromorphic image sensor array features photon-triggered synaptic plasticity owing to its quasi-linear time-dependent photocurrent generation and prolonged photocurrent decay, originated from charge trapping in the MoS₂-organic vertical stack. The curved neuromorphic image sensor array integrated with a plano-convex lens derives a pre-processed image from a set of noisy optical inputs without redundant data storage, processing, and communications as well as without complex optics. The proposed imaging device can substantially improve efficiency of the image acquisition and recognition process, a step forward to the next generation machine vision.

Designing efficient bio-inspired visual recognition system remains a challenge. Here the authors present a curved neuromorphic image sensor array based on a heterostructure of MoS2 and pV3D3 integrated with a plano-convex lens for efficient image acquisition and data pre-processing.

Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO_{2- X} switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.

Multi-Level Analog Resistive Switching Characteristics in Tri-Layer HfO2/Al2O3/HfO2 Based Memristor on ITO Electrode

Atomic layer deposited (ALD) HfO2/Al2O3/HfO2 tri-layer resistive random access memory (RRAM) structure has been studied with a transparent indium tin oxide (ITO) transparent electrode. Highly stable and reliable multilevel conductance can be controlled by the set current compliance and reset stop voltage in bipolar resistive switching. Improved gradual resistive switching was achieved because of the interdiffusion in the HfO2/Al2O3 interface where tri-valent Al incorporates with HfO2 and produces HfAlO. The uniformity in bipolar resistive switching with Ion/Ioff ratio (>10) and excellent endurance up to >103 cycles was achieved. Multilevel conductance levels in potentiation/depression were realized with constant amplitude pulse train and increasing pulse amplitude. Thus, tri-layer structure-based RRAM can be a potential candidate for the synaptic device in neuromorphic computing.

Enhancing Short-Term Plasticity by Inserting a Thin TiO2 Layer in WOx-Based Resistive Switching Memory

In this work, we emulate biological synaptic properties such as long-term plasticity (LTP) and short-term plasticity (STP) in an artificial synaptic device with a TiN/TiO2/WOx/Pt structure. The graded WOx layer with oxygen vacancies is confirmed via X-ray photoelectron spectroscopy (XPS) analysis. The control TiN/WOx/Pt device shows filamentary switching with abrupt set and gradual reset processes in DC sweep mode. The TiN/WOx/Pt device is vulnerable to set stuck because of negative set behavior, as verified by both DC sweep and pulse modes. The TiN/WOx/Pt device has good retention and can mimic long-term memory (LTM), including potentiation and depression, given repeated pulses. On the other hand, TiN/TiO2/WOx/Pt devices show non-filamentary type switching that is suitable for fine conductance modulation. Potentiation and depression are demonstrated in the TiN/TiO2 (2 nm)/WOx/Pt device with moderate conductance decay by application of identical repeated pulses. Short-term memory (STM) is demonstrated by varying the interval time of pulse inputs for the TiN/TiO2 (6 nm)/WOx/Pt device with a quick decay in conductance.

Vertical organic synapse expandable to 3D crossbar array

Recently, three-terminal synaptic devices have attracted considerable attention owing to their nondestructive weight-update behavior, which is attributed to the completely separated terminals for reading and writing. However, the structural limitations of these devices, such as a low array density and complex line design, are predicted to result in low processing speeds and high energy consumption of the entire system. Here, we propose a vertical three-terminal synapse featuring a remote weight update via ion gel, which is also extendable to a crossbar array structure. This synaptic device exhibits excellent synaptic characteristics, which are achieved via precise control of ion penetration onto the vertical channel through the weight-control terminal. Especially, the applicability of the developed vertical organic synapse array to neuromorphic computing is demonstrated using a simple crossbar synapse array. The proposed synaptic device technology is expected to be an important steppingstone to the development of high-performance and high-density neural networks.

Memory technology-a primer for material scientists

From our own experience, we know that there is a gap to bridge between the scientists focused on basic material research and their counterparts in a close-to-application community focused on identifying and solving final technological and engineering challenges. In this review, we try to provide an easy-to-grasp introduction to the field of memory technology for materials scientists. An understanding of the big picture is vital, so we first provide an overview of the development and architecture of memories as part of a computer and call attention to some basic limitations that all memories are subject to. As any new technology has to compete with mature existing solutions on the market, today's mainstream memories are explained, and the need for future solutions is highlighted. The most prominent contenders in the field of emerging memories are introduced and major challenges on their way to commercialization are elucidated. Based on these discussions, we derive some predictions for the memory market to conclude the paper.

Double-Gate MoS2 Field-Effect Transistor with a Multilayer Graphene Floating Gate: A Versatile Device for Logic, Memory, and Synaptic Applications

2D materials with low-temperature processing hold promise for electronic devices that augment conventional silicon technology. To meet this promise, devices should have capabilities not easily achieved with silicon technology, including planar fully-depleted silicon-on-insulator with substrate body-bias, or vertical finFETs with no body-bias capability. In this work, we fabricate and characterize a device [a double-gate MoS₂ field-effect transistor (FET) with hexagonal boron nitride (h-BN) gate dielectrics and a multi-layer graphene floating gate (FG)] in multiple operating conditions to demonstrate logic, memory, and synaptic applications; a range of h-BN thicknesses is investigated for charge retention in the FG. In particular, we demonstrate this device as a (i) logic FET with adjustable V_T by charges stored in the FG, (ii) digital flash memory with lower pass-through voltage to enable improved reliability, and (iii) synaptic device with decoupling of tunneling and gate dielectrics to achieve a symmetric program/erase conductance change. Overall, this versatile device, compatible to back-end-of-line integration, could readily augment silicon technology.

Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application

Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.

Heterogeneous stimuli induced nonassociative learning behavior in ZnO nanowire memristor

Nonassociative learning is a biologically essential and evolutionarily adaptive behavior in organisms. The bionic simulation of nonassociative learning based on electronic devices is essential to the neuromorphic computing. In this work, nonassociative learning is mimicked by a ZnO nanowire memristor without any other peripheral control circuit. The memristor demonstrates habituation and sensitization behaviors at the electrical and optical stimuli. Typical network-level parametric characteristics of habituation in neuroscience are realized in the memristor. When the heterogeneous stimuli are applied coincidentally, sensitization pulse could be identified by the exceptional response current. The results show that the natural selection rules could be simulated by the current single memristor. A possible mechanism based on the trapping states and adsorption of oxygen at the interface of Au/ZnO is proposed. The implementation of nonassociative learning in a single memristor device paves the way for building neuromorphic systems by simple electronic devices.

Optoelectronic Synapses Based on Hot-Electron-Induced Chemical Processes

Highly efficient information processing in the brain is based on processing and memory components called synapses, whose output is dependent on the history of the signals passed through them. Here, we have developed an artificial synapse with both electrical and optical memory effects using chemical transformations in plasmonic tunnel junctions. In an electronic implementation, the electrons tunneled into plasmonic nanorods under a low bias voltage are harvested to write information into the tunnel junctions via hot-electron-mediated chemical reactions with the environment. In an optical realization, the information can be written by an external light illumination to excite hot electrons in the plasmonic nanorods. The stored information is nonvolatile and can be read either electrically or optically by measuring the resistance or inelastic-tunneling-induced light emission, respectively. The described architecture provides a high density (∼10¹⁰ cm^-2) of memristive optoelectronic devices which can be used as multilevel nonvolatile memory, logic units, or artificial synapses in future electronic, optoelectronic, and artificial neural networks.

Gate-Tunable Synaptic Dynamics of Ferroelectric-Coupled Carbon-Nanotube Transistors

Artificial neural networks (ANNs) based on synaptic devices, which can simultaneously perform processing and storage of data, have superior computing performance compared to conventional von Neumann architectures. Here, we present a ferroelectric coupled artificial synaptic device with reliable weight update and storage properties for ANNs. The artificial synaptic device, which is based on a ferroelectric polymer capacitively coupled with an oxide dielectric via an electric-field-permeable, semiconducting single-walled carbon-nanotube channel, is successfully fabricated by inkjet printing. By controlling the ferroelectric polarization, synaptic dynamics, such as excitatory and inhibitory postsynaptic currents and long-term potentiation/depression characteristics, is successfully implemented in the artificial synaptic device. Furthermore, the constructed ANN, which is designed in consideration of the device-to-device variation within the synaptic array, efficiently executes the tasks of learning and recognition of the Modified National Institute of Standards and Technology numerical patterns.

Ferroelectric Tunneling Junctions Based on Aluminum Oxide/ Zirconium-Doped Hafnium Oxide for Neuromorphic Computing

Ferroelectric tunneling junctions (FTJs) with tunable tunneling electroresistance (TER) are promising for many emerging applications, including non-volatile memories and neurosynaptic computing. One of the key challenges in FTJs is the balance between the polarization value and the tunneling current. In order to achieve a sizable on-current, the thickness of the ferroelectric layer needs to be scaled down below 5 nm. However, the polarization in these ultra-thin ferroelectric layers is very small, which leads to a low tunneling electroresistance (TER) ratio. In this paper, we propose and demonstrate a new type of FTJ based on metal/Al₂O₃/Zr-doped HfO₂/Si structure. The interfacial Al₂O₃ layer and silicon substrate enable sizable TERs even when the thickness of Zr-doped HfO₂ (HZO) is above 10 nm. We found that F-N tunneling dominates at read voltages and that the polarization switching in HZO can alter the effective tunneling barrier height and tune the tunneling resistance. The FTJ synapses based on Al₂O₃/HZO stacks show symmetric potentiation/depression characteristics and widely tunable conductance. We also show that spike-timing-dependent plasticity (STDP) can be harnessed from HZO based FTJs. These novel FTJs will have high potential in non-volatile memories and neural network applications.

Ferroelectric Tunneling Junctions Based on Aluminum Oxide/ Zirconium-Doped Hafnium Oxide for Neuromorphic Computing

Ferroelectric tunneling junctions (FTJs) with tunable tunneling electroresistance (TER) are promising for many emerging applications, including non-volatile memories and neurosynaptic computing. One of the key challenges in FTJs is the balance between the polarization value and the tunneling current. In order to achieve a sizable on-current, the thickness of the ferroelectric layer needs to be scaled down below 5 nm. However, the polarization in these ultra-thin ferroelectric layers is very small, which leads to a low tunneling electroresistance (TER) ratio. In this paper, we propose and demonstrate a new type of FTJ based on metal/Al₂O₃/Zr-doped HfO₂/Si structure. The interfacial Al₂O₃ layer and silicon substrate enable sizable TERs even when the thickness of Zr-doped HfO₂ (HZO) is above 10 nm. We found that F-N tunneling dominates at read voltages and that the polarization switching in HZO can alter the effective tunneling barrier height and tune the tunneling resistance. The FTJ synapses based on Al₂O₃/HZO stacks show symmetric potentiation/depression characteristics and widely tunable conductance. We also show that spike-timing-dependent plasticity (STDP) can be harnessed from HZO based FTJs. These novel FTJs will have high potential in non-volatile memories and neural network applications.