Fully solution-processed transparent artificial neural network using drop-on-demand electrohydrodynamic printing

Jason Yong, You Liang, Yang Yu, Basem Hassan, Md Sharafat Hossain, Kumaravelu Ganesan, Ranjith Rajasekharan Unnithan, Robin Evans, Gary Egan, Gursharan Chana, Babak Nasr, Efstratios Skafidas

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)17521-17530
Number of pages10
JournalACS Applied Materials & Interfaces
Issue number19
Publication statusPublished - 15 May 2019


  • electrohydrodynamic printing
  • neuromorphic device
  • sol-gel InO
  • sol-gel ITO
  • synaptic plasticity
  • synaptic transistors
  • thin film transistor

Cite this

Yong, J., Liang, Y., Yu, Y., Hassan, B., Hossain, M. S., Ganesan, K., Unnithan, R. R., Evans, R., Egan, G., Chana, G., Nasr, B., & Skafidas, E. (2019). Fully solution-processed transparent artificial neural network using drop-on-demand electrohydrodynamic printing. ACS Applied Materials & Interfaces, 11(19), 17521-17530. https://doi.org/10.1021/acsami.9b02465