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

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Memristive and nanoionic devices have recently emerged as leading candidates for neuromorphic computing architectures. While top-down fabrication based on conventional bulk materials has enabled many early neuromorphic devices and circuits, bottom-up approaches based on low-dimensional nanomaterials have shown novel device functionality that often better mimics a biological neuron. In addition, the chemical, structural and compositional tunability of low-dimensional nanomaterials coupled with the permutational flexibility enabled by van der Waals heterostructures offers significant opportunities for artificial neural networks. In this Review, we present a critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic nanoelectronic materials in comparison with more mature technologies based on traditional bulk electronic materials. This Review highlights the progress made towards the development of neuromorphic devices and architectures enabled by low-dimensional nanomaterials

Neuromorphic nanoelectronic materials

DOI: 10.1002/admt.201800628 applications (e.g., soft robotics, medical devices).[1–6] Despite state-of-the-art bulkbased planar integrated-circuit devices, their rigid and brittle nature gives rise to the incompatibility with curvilinear and soft human bodies, restricting the development of newborn human-friendly interactive electronics. In contrast, the bendable and flexible wearable electronics could be conformally attached onto human bodies almost without discomfort and succeed in performing a great deal of sensing functionalities. Realization of such promising goals requires the flexible sensor platforms provided with crucial characteristics of light weight, ultrathinness, superior flexibility, stretchability, high sensitivity as well as rapid response.[7–10] Inspired by the perceptive features of human skins, the wearable sensor systems are capable of acquiring abundant information from the external environment with the assistance of sensing modules, such as pressure sensors, strain sensors, temperature sensors, etc.[11] A typical example is their application in prosthetics that could afford the capacity to perceive touch or temperature for the disabled.[12] Additionally, the wearable sensor systems are able to identify physical or chemical signals produced by the human body, providing promising opportunities to evaluate health states.[5,13–15] Conventional skin-like sensor platforms primarily comprise one or two sensing modules, data processing units, and power supplies. Their unitary functionality, however, cannot satisfy the increasing demands of IoTs. Recently, the rapid advances in novel sensing materials, fabrication strategies, and innovative electronic constitution contribute to the development of versatile integration of multimodal sensors, which could synchronously distinguish diverse stimuli from the complex environment and monitor multiple vital signs from the human body.[16,17] In spite of several attempts done in terms of such multimodal sensor systems, one of the cumbersome issues originates from the crosscoupling effect among different categories of signals simultaneously generated by various sensors. Furthermore, the skin-like multiple sensor systems usually suffer from the limited number of repeated use, resulting in their high use-cost. The development of separable versatile devices may address this issue with one layer realized by costeffective materials and fabrication manners for disposable use and the other composed of relatively expensive components for repeatable applications.[18] Additionally, the multiple bending or Skin-inspired wearable devices hold great potentials in the next generation of smart portable electronics owing to their intriguing applications in healthcare monitoring, soft robotics, artificial intelligence, and human–machine interfaces. Despite tremendous research efforts dedicated to judiciously tailoring wearable devices in terms of their thickness, portability, flexibility, bendability as well as stretchability, the emerging Internet of Things demand the skininterfaced flexible systems to be endowed with additional functionalities with the capability of mimicking skin-like perception and beyond. This review covers and highlights the latest advances of burgeoning multifunctional wearable electronics, primarily including versatile multimodal sensor systems, self-healing material-based devices, and self-powered flexible sensors. To render the penetration of human-interactive devices into global markets and households, economical manufacturing techniques are crucial to achieve large-scale flexible systems with high-throughput capability. The booming innovations in this research field will push the scientific community forward and benefit human beings in the near future.

Multifunctional Skin-Inspired Flexible Sensor Systems for Wearable Electronics

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.

A comprehensive review on emerging artificial neuromorphic devices

The strong and controllable chemical sensitivity of organic semiconductors (OSCs) and the amplification capability of transistors in circuits make use of OSC-based field-effect transistors compelling for chemical sensors. Analytes detected and assayed range from few-atom gas-phase molecules that may have adverse health and security implications to biomacromolecules (proteins, nucleic acids) that may be markers for physiological processes and medical conditions. This review highlights recent progress in organic field-effect transistor (OFET) chemical sensors, emphasizing advances from the past 5 years and including aspects of OSC morphology and the role of adjacent dielectrics. Design elements of the OSCs and various formats for the devices are illustrated and evaluated. Challenges associated with the present state of the art and future opportunities are also discussed.

Chemical and Biomolecule Sensing with Organic Field-Effect Transistors

Small‐Molecule‐Based Organic Field‐Effect Transistor for Nonvolatile Memory and Artificial Synapse

Organic Flexible Electronics

Functional emulation of biological synapses using electronic devices is regarded as the first step toward neuromorphic engineering and artificial neural networks (ANNs). Electrolyte-gated transistors (EGTs) are mixed ionic–electronic conductivity devices capable of efficient gate-channel capacitance coupling, biocompatibility, and flexible architectures. Electrolyte gating offers significant advantages for the realization of neuromorphic devices/architectures, including ultralow-voltage operation and the ability to form parallel-interconnected networks with minimal hardwired connectivity. In this review, the most recent developments in EGT-based electronics are introduced with their synaptic behaviors and detailed mechanisms, including short-/long-term plasticity, global regulation phenomena, lateral coupling between device terminals, and spatiotemporal correlated functions. Analog memory phenomena allow for the implementation of perceptron-based ANNs. Due to their mixed-conductivity phenomena, neuromorphic circuits based on EGTs allow for facile interfacing with biological environments. We also discuss the future challenges in implementing low power, high speed, and reliable neuromorphic computing for large-scale ANNs with these neuromorphic devices. The advancement of neuromorphic devices that rely on EGTs highlights the importance of this field for neuromorphic computing and for novel healthcare technologies in the form of adaptable or trainable biointerfacing.

Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing

Conductive filaments (CFs)-based resistive random access memory possesses the ability of scaling down to sub-nanoscale with high-density integration architecture, making it the most promising nanoelectronic technology for reclaiming Moore's law. Compared with the extensive study in inorganic switching medium, the scientific challenge now is to understand the growth kinetics of nanoscale CFs in organic polymers, aiming to achieve controllable switching characteristics toward flexible and reliable nonvolatile organic memory. Here, this paper systematically investigates the resistive switching (RS) behaviors based on a widely adopted vertical architecture of Al/organic/indium-tin-oxide (ITO), with poly(9-vinylcarbazole) as the case study. A nanoscale Al filament with a dynamic-gap zone (DGZ) is directly observed using in situ scanning transmission electron microscopy (STEM) , which demonstrates that the RS behaviors are related to the random formation of spliced filaments consisting of Al and oxygen vacancy dual conductive channels growing through carbazole groups. The randomicity of the filament formation can be depressed by introducing a cone-shaped contact via a one-step integration method. The conical electrode can effectively shorten the DGZ and enhance the localized electric field, thus reducing the switching voltage and improving the RS uniformity. This study provides a deeper insight of the multiple filamentary mechanisms for organic RS effect.

Controllable Organic Resistive Switching Achieved by One-Step Integration of Cone-Shaped Contact.

Despite remarkable advances in the development of organic field-effect transistor (OFET) memories over recent years, the charge trapping elements remain confined to the critical electrets of polymers, nanoparticles, or ferroelectrics. Nevertheless, rare reports are available on the complementary advantages of different types of trapping elements integrated in one single OFET memory. To address this issue, we fabricated two kinds of pentacene-based OFET memories with solution-processed amorphous and β-phase poly(9,9-dioctylfluorene) (PFO) films as charge trapping layers, respectively. Compared to the amorphous film, the β-PFO film has self-doped nanostructures (20–120 nm) and could act as natural charge trapping elements, demonstrating the synergistic effects of combining both merits of polymer and nanoparticles into one electret. Consequently, the OFET memory with β-PFO showed nearly 26% increment in the storage capacity and a pronounced memory window of ∼45 V in 20 ms programming time. Besides, the reten...

Haifeng Ling

Papers

Small‐Molecule‐Based Organic Field‐Effect Transistor for Nonvolatile Memory and Artificial Synapse

Organic Flexible Electronics

Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing

Controllable Organic Resistive Switching Achieved by One-Step Integration of Cone-Shaped Contact.

Synergistic Effects of Self-Doped Nanostructures as Charge Trapping Elements in Organic Field Effect Transistor Memory.