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

• DOI: 10.1021/acsami.9b02465
• Publication Date: 2019-04-22T11:07:16Z
• Authors: Jason Yong; You Liang; Yang Yu; Basem Hassan; Md Sharafat Hossain; Kumaravelu Ganesan; Ranjith Rajasekha...; Robin Evans; Gary Egan; Gursharan Chana; Babak Nasr; Efstratios Skafidas
• Keywords: Neuromorphic engineering; Semiconductor device fabrication; Nanomanufacturing

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