Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, WeinheimFlexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics

Requirements and recent advances in research on organic neuroelectronics are outlined herein. Neuroelectronics such as neural interfaces and neuroprosthetics provide a promising approach to diagnose and treat neurological diseases. However, the current neural interfaces are rigid and not biocompatible, so they induce an immune response and deterioration of neural signal transmission. Organic materials are promising candidates for neural interfaces, due to their mechanical softness, excellent electrochemical properties, and biocompatibility. Also, organic nervetronics, which mimics functional properties of the biological nerve system, is being developed to overcome the limitations of the complex and energy-consuming conventional neuroprosthetics that limit long-term implantation and daily-life usage. Examples of organic materials for neural interfaces and neural signal recordings are reviewed, recent advances of organic nervetronics that use organic artificial synapses are highlighted, and then further requirements for neuroprosthetics are discussed. Finally, the future challenges that must be overcome to achieve ideal organic neuroelectronics for next-generation neuroprosthetics are discussed.N

Organic synaptic transistors for flexible and stretchable artificial sensory nerves

This article reviews artificial nerve electronics (nervetronics), an emerging field in which the goal is to develop bioinspired electronics that implement biological sensory functions. An artificial synapse is a fundamental core technology of artificial sensory nerves that can emulate functional properties of a biological synapse. Use of artificial synapses reduces the energy consumption and increases the sensitivity of low-level perception in artificial sensory nerves. Wearable and implantable devices require artificial sensory nerves that are flexible and stretchable. Therefore, development of organic artificial synapses that have these qualities is a central focus in nervetronics. Here, we review the concept and mechanism of organic artificial synapses for use as basic elements of flexible and stretchable artificial nerves. Next, we outline the research direction of the flexible and stretchable artificial sensory nerves so far, and finally, identify challenges of artificial sensory nerves that must be solved to enable actual application of this developing technology.

Achieving Microstructure-Controlled Synaptic Plasticity and Long-Term Retention in Ion-Gel-Gated Organic Synaptic Transistors

Organic synaptic transistors using intrinsic (i.e., non-doped) organic semiconductors have demonstrated various synaptic functions to mimic biological synapses, but the devices show limited long-term retention behaviors although long-term memory is essential for neuromorphic computing. To achieve long-term retention time, correlating the synaptic responses with the microstructures of polymer semiconductor is an imperative step. It is shown that synaptic plasticity in ion-gel-gated organic synaptic transistors (IGOSTs) can be modulated by controlling the microstructure of organic semiconductors and that long-term memory retention can be significantly prolonged by increasing their crystallinity. The crystallinity of poly(3-hexylthiophene-2,5-diyl) (P3HT) films that are spun-cast on bare and self-assembled monolayer is systematically controlled, before and after thermal treatments. Long-term retention tends to extend, as the crystallinity increases. To evaluate synaptic current decay behaviors, it is suggested that the relaxation is a result of de-doping of the polymer semiconductor over time. The recognition of handwritten digits is simulated and a high classification accuracy (>92%) is achieved with IGOSTs including high crystalline P3HT film. The study provides fundamental information about the effects of polymer microstructure on synaptic plasticity of IGOSTs, which may be applicable in neuromorphic electronics.

A low-power stretchable neuromorphic nerve with proprioceptive feedback

By relaying neural signals from the motor cortex to muscles, devices for neurorehabilitation can enhance the movement of limbs in which nerves have been damaged as a consequence of injuries affecting the spinal cord or the lower motor neurons. However, conventional neuroprosthetic devices are rigid and power-hungry. Here we report a stretchable neuromorphic implant that restores coordinated and smooth motions in the legs of mice with neurological motor disorders, enabling the animals to kick a ball, walk or run. The neuromorphic implant acts as an artificial efferent nerve by generating electrophysiological signals from excitatory post-synaptic signals and by providing proprioceptive feedback. The device operates at low power (~1/150 that of a typical microprocessor system), and consists of hydrogel electrodes connected to a stretchable transistor incorporating an organic semiconducting nanowire (acting as an artificial synapse), connected via an ion gel to an artificial proprioceptor incorporating a carbon nanotube strain sensor (acting as an artificial muscle spindle). Stretchable electronics with proprioceptive feedback may inspire the further development of advanced neuromorphic devices for neurorehabilitation.N

Dimensionality Dependent Plasticity in Halide Perovskite Artificial Synapses for Neuromorphic Computing

The hysteretic behavior of organic-inorganic halide perovskites (OHPs) are exploited for application in neuromorphic electronics. Artificial synapses with 2D and quasi-2D perovskite are demonstrated that have a bulky organic cation (phenethylammonium (PEA)) to form structures of (PEA)(2)MA(n)(-1)Pb(n)Br(3)(n)(+1). The OHP films have morphological properties that depend on their structure dimensionality (i.e., n value), and artificial synapses fabricated from them show synaptic responses such as short-term plasticity, paired-pulse facilitation, and long-term plasticity. The operation mechanism of OHP artificial synapses are also analyzed depending on the dimensionality and it is found that quasi-2D (n = 3-5) OHP artificial synapses show much longer retention than 2D and 3D OHP counterparts. The calculated energy consumption of a 2D OHP artificial synapse (approximate to 0.7 fJ per synaptic event) is comparable to that of biological synapses (1-10 fJ per synaptic event). These OHP artificial synapses may enable development of neuromorphic electronics that use very little energy.N

Versatile neuromorphic electronics by modulating synaptic decay of single organic synaptic transistor: From artificial neural networks to neuro-prosthetics

Organic neuromorphic electronics are inspired by a biological nervous system. Bio-inspired computing mimics learning and memory in a brain (i.e., the central nervous system), and bio-inspired soft robotics and nervous prosthetics mimics the neural signal transmission of afferent/efferent nerves (i.e., the peripheral nervous system). Synaptic decay time of nerves differ among biological organs, so the decay time of artificial synapses should be tuned for their specific uses in neuro-inspired electronics. However, controlling a synaptic decay constant in a fixed synaptic device geometry for broad applications was not been achieved in previous research of neuromorphic electronic devices despite the importance to achieve broad applications from neuromorphic computing to neuro-prosthetics. Here, we tailored the synaptic decay constant of organic synaptic transistors with fixed materials and devices structure rather than changing the form of presynaptic spikes, which enabled broad applications from neuromorphic computing to neuro-prosthetics. To achieve this, the relation between crystallinity of the polymer semiconductor film and the synaptic decay constant was revealed. The crystallinity of the polymer controlled electrochemical-doping kinetics and resultant synaptic behaviors of artificial synaptic transistors. In this way, we demonstrated not only long-term retention for learning and memory that is useful for neuromorphic computing in ion-gel gated organic synaptic transistor (IGOST) but also the short-term retention for fast synaptic transmission that is useful for emulating peripheral nerves such as sensory and motor nerve. To prove the feasibility of our approach in a two different ways, we first simulated pattern recognition on the MNIST dataset of handwritten digits using an IGOST with long-term retention due to increased crystallinity and then, developed artificial auditory sensory nerves that combines an IGOST with short term retention due to disordered chain morphology in a polymer semiconductor, with a triboelectric acoustic sensor. We expect that our approach will provide a universal strategy to realize wide neuromorphic electronic applications