Fully Solution-Processed Transparent Artificial Neural Network Using Drop-On-Demand Electrohydrodynamic Printing

Abstract

Artificial neural networks (ANN), deep learning, and neuromorphic systems are exciting new processing architectures being used to implement a wide variety of intelligent and adaptive systems. To date, these architectures have been primarily realized using traditional complementary metal–oxide–semiconductor (CMOS) processes or otherwise conventional semiconductor fabrication processes. Thus, the high cost associated with the design and fabrication of these circuits has limited the broader scientific community from applying new ideas, and arguably, has slowed research progress in this exciting new area. Solution-processed electronics offer an attractive option for providing low-cost rapid prototyping of neuromorphic devices. This article proposes a novel, wholly solution-based process used to produce low-cost transparent synaptic transistors capable of emulating biological synaptic functioning and thus used to construct ANN. We have demonstrated the fabrication process by constructing an ANN that encodes and decodes a 100 × 100 pixel image. Here, the synaptic weights were configured to achieve the desired image processing functions.

Recent advances in printable thermoelectric devices: materials, printing techniques, and applications

“…By using high-frequency pulses for the EHD system, a high spatial resolution down to 100 nm was achieved. 49 Using this technique, researchers have printed thin-lm transistors, 50 light-emitting diodes, 51 memristors, 9 articial neural networks 10 and QLEDs. 52 The main advantage of dispenser printing is low material wastage and mask free patterning.…”

Artificial Biosystems by Printing Biology

“…Recently, sodium alginate-gated In2O3-gated transistor has been reported in the literature for the realization of artificial neuronal networks. [247] These printed electronics were applied for image processing operations, implementing with success color filter algorithms. The working principle is based on the hydrogenation and hydroxylation of In2O3 surface, which introduces profound hysteresis properties in the fabricated transistors; then, hysteresis provides short-term synaptic plasticity capabilities, which can be exploited to imitate synaptic functions ( Figure 11A).…”

“…Reproduced with permission from Ref. [247] Copyright 2019, American Chemical Society. (B) Diffractive deep neural networks (D2NNs) comprising multiple transmissive (or reflective) layers, where each point on a given layer acts as a neuron, with a complex-valued transmission (or reflection) coefficient.…”

Flexible and Energy-efficient Synaptic Transistor with Quasi-linear Weight Update Protocol by Inkjet Printing of Orientated Polar-electret/High-k Oxide Hybrid Dielectric

“…The attempts of inkjet printing mainly focus on the electrodes (PEO:P3HT 4 , etc.) and the channel layers (sc-SWCNTs 5,6 , P(VP-EDMAEMAES) 7 , In 2 O 3 8 , ITO 9 etc.) in synaptic transistors, while to fabricate the dielectrics is blocked by the basic need of analog multi-state weight update in biological like protocol.…”