In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.

https://iopscience.iop.org/article/10.1088/0957-4484/24/38/382001/pdf

Synaptic electronics: materials, devices and applications

Recent progress in electronic skin or e-skin research is broadly reviewed, focusing on technologies needed in three main applications: skin-attachable electronics, robotics, and prosthetics. First, since e-skin will be exposed to prolonged stresses of various kinds and needs to be conformally adhered to irregularly shaped surfaces, materials with intrinsic stretchability and self-healing properties are of great importance. Second, tactile sensing capability such as the detection of pressure, strain, slip, force vector, and temperature are important for health monitoring in skin attachable devices, and to enable object manipulation and detection of surrounding environment for robotics and prosthetics. For skin attachable devices, chemical and electrophysiological sensing and wireless signal communication are of high significance to fully gauge the state of health of users and to ensure user comfort. For robotics and prosthetics, large-area integration on 3D surfaces in a facile and scalable manner is critical. Furthermore, new signal processing strategies using neuromorphic devices are needed to efficiently process tactile information in a parallel and low power manner. For prosthetics, neural interfacing electrodes are of high importance. These topics are discussed, focusing on progress, current challenges, and future prospects.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201904765

Electronic Skin: Recent Progress and Future Prospects for Skin‐Attachable Devices for Health Monitoring, Robotics, and Prosthetics

The basic units in our brain are neurons, and each neuron has more than 1,000 synapse connections. Synapse is the basic structure for information transfer in an ever-changing manner, and short-term plasticity allows synapses to perform critical computational functions in neural circuits. Therefore, the major challenge for the hardware implementation of neuromorphic computation is to develop artificial synapse network. Here in-plane lateral-coupled oxide-based artificial synapse network coupled by proton neurotransmitters are self-assembled on glass substrates at room-temperature. A strong lateral modulation is observed due to the proton-related electrical-double-layer effect. Short-term plasticity behaviours, including paired-pulse facilitation, dynamic filtering and spatiotemporally correlated signal processing are mimicked. Such laterally coupled oxide-based protonic/electronic hybrid artificial synapse network proposed here is interesting for building future neuromorphic systems.

/pdf/artificial-synapse-network-on-inorganic-proton-conductor-for-c1yspzlnuk.pdf

Artificial synapse network on inorganic proton conductor for neuromorphic systems

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.

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.

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

A carbon nanotube (CNT) synapse emulates a biological synapse with its dynamic logic, learning, and memory functions induced by the interactions between CNTs and hydrogen ions in an electrochemical cell. A circuit of CNT synapses operates with extremely low-energy consumption and could potentially emulate the functions of the neuronal network.

A Carbon Nanotube Synapse with Dynamic Logic and Learning

Recent developments in chitosan hydrogels carrying natural bioactive compounds

A nonvolatile analog memory transistor is demonstrated by integrating C60 molecules as charge storage molecules in the transistor gate, and carbon nanotubes (CNTs) in the transistor channel. The currents through the CNT channel can be tuned quantitatively and reversibly to analog values by controlling the number of electrons trapped in the C60 molecules. After tuning, the electrons trapped in the C60 molecules in the gate, and the current through the CNT channel, can be preserved in a nonvolatile manner, indicating the characteristics of the nonvolatile analog memory.

Nonvolatile analog memory transistor based on carbon nanotubes and C60 molecules.

A spiking neuron circuit based on a carbon nanotube (CNT) transistor is presented in this paper. The spiking neuron circuit has a crossbar architecture in which the transistor gates are connected to its row electrodes and the transistor sources are connected to its column electrodes. An electrochemical cell is incorporated in the gate of the transistor by sandwiching a hydrogen-doped poly(ethylene glycol)methyl ether (PEG) electrolyte between the CNT channel and the top gate electrode. An input spike applied to the gate triggers a dynamic drift of the hydrogen ions in the PEG electrolyte, resulting in a post-synaptic current (PSC) through the CNT channel. Spikes input into the rows trigger PSCs through multiple CNT transistors, and PSCs cumulate in the columns and integrate into a 'soma' circuit to trigger output spikes based on an integrate-and-fire mechanism. The spiking neuron circuit can potentially emulate biological neuron networks and their intelligent functions.

https://iopscience.iop.org/article/10.1088/0957-4484/23/27/275202/pdf

A spiking neuron circuit based on a carbon nanotube transistor

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201370080

Quyen Truong

Papers

A Carbon Nanotube Synapse with Dynamic Logic and Learning

Recent developments in chitosan hydrogels carrying natural bioactive compounds

Nonvolatile analog memory transistor based on carbon nanotubes and C60 molecules.

A spiking neuron circuit based on a carbon nanotube transistor

A Carbon Nanotube Synapse with Dynamic Logic and Learning (Adv. Mater. 12/2013)