Inkjet-printed stretchable and low voltage synaptic transistor array. Parallel weight update protocol for a carbon nanotube synaptic transistor array for accelerating neuromorphic computing. Flexible carbon nanotube synaptic transistor for neurological electronic skin applications. Aligned carbon nanotube synaptic transistors for large-scale neuromorphic computing. Synaptic transistors with aluminum oxide dielectrics enabling full audio frequency range signal processing. Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks. Flexible metal oxide/graphene oxide hybrid neuromorphic transistors on flexible conducting graphene substrates. Recent progress in three-terminal artificial synapses: From device to system. Optoelectronic resistive random access memory for neuromorphic vision sensors. A million spiking-neuron integrated circuit with a scalable communication network and interface. Toward high-performance digital logic technology with carbon nanotubes. Synaptic electronics: Materials, devices and applications. The future of electronics based on memristive systems. By taking advantage of the long retention time of the trapped charges and uniform device-to-device performance, long-term image memory behavior of neural network was successfully imitated in a CNT synaptic transistor array. Large dynamic range of STDP (> 2,180) and low power consumption per spike (∼ 0.7 pJ) were achieved. Basic synaptic functions including inhibitory post-synaptic current (IPSC), excitatory post-synaptic current (EPSC), spike-timing-dependent plasticity (STDP), and paired-pulse facilitation (PPF) were mimicked. Large synaptic weight update was realized due to the high sensitivity of the CNTs to the trapped charges in vicinity. In this paper, synaptic transistors were fabricated by using carbon nanotube (CNT) thin films and interface charge trapping effects were confirmed to dominate the weight update of the synaptic transistors. To this end, the first step is to mimic functions of biological neurons and synapses by electronic devices. Brain-inspired neuromorphic computing is expected for breaking through the bottleneck of the computer of conventional von Neumann architecture.
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