Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Abstract

Flexible 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.

Reconfigurable Multifunctional Ambipolar Polymer Transistors with Improved Switching-off Capability Upon Non-Uniformly Distributed Compensation Potential

“…When the nerve impulses are transmitted to the synaptosomes through the axon, the presynaptic membrane releases neurotransmitters to the synaptic cleft, and the neurotransmitters diffuse to the post-synaptic membrane and bind to protein receptors, causing excitatory or inhibitory changes in the post-synaptic membrane (Fig. 4d) 14,40,68 . The function of synapse could be emulated in ambipolar transistor with electret, where holes or electrons serving as the transmitters could be trapped in the electret under the gate pulse to convert the gate pulse carried signals into the change of drain current (Fig.…”

“…The STM transforms to LTM when the stimulations are persistent or repeated, as the EPSC is substantially enhanced after 10 or 50 consecutive identical pulses and retains for longer time at high values. Long-term potentiation and depression (LTP, LTD) are also accomplished in the synaptic transistor, which is useful features for neuromorphic computing 17,40,68 . Figure 4j shows the PSC of the synaptic transistor after a series of excitatory spikes and inhibitory spikes.…”

Organic materials and devices for brain-inspired computing: From artificial implementation to biophysical realism

“…This combination of hardware and biological systems constitutes an unconventional type of hybrid intelligent agent that merges the two domains. [15][16][17][18] Such a hybrid approach is requisite for the local processing of biological signals, extraction of specific patterns, and realization of devices that act upon the environment in a biologically relevant fashion. 18 This bidirectionality requires that both domains, hardware and biological, speak a similar language, yet operate on the same time scale to enable real-time interaction.…”

A Bioinspired Stretchable Sensory-Neuromorphic System

“…Stretchable electronic devices have been widely considered as individual modules for smart skin prosthetics owing to their exceptional electrical and mechanical reliability in dynamic environments; these features enable the monitoring of various external stimuli such as touch, heat, and humidity, and delivery of processed information to sensory nerves [1][2][3][4] . Although much effort has been directed at the development of multifunctional systems comprising various active electronic components [5][6][7] , further progress in this eld remains limited by the absence of the monolithic integration of neuromorphic constituents into individual sensing and actuating components. Thus, new methodology allowing one to overcome this insuperable problem is highly sought after.…”