Additive Manufacturing Organic Neuromorphic Devices and Neural Networks

Organic electrochemical transistors (OECTs) are being explored as neuromorphic devices, where they emulate characteristics of biological synapses through the co- location of information storage and processing on the same unit, overcoming the von Neumann performance bottleneck. Applications of OECT technology have mainly been sought after in bioelectronics, enabling human machine interfacing for smart sensing and monitoring applications in biological systems. To achieve the long-term vision of translating OECT based bioelectronics to inexpensive, low-power computational devices, there is a need to develop easily adaptable and scalable digital fabrication techniques. In this thesis, a study of low-cost additive manufacturing techniques to fabricating neuromorphic OECTs and neural networks is carried out. Three major findings are presented in this thesis. First, it is shown that the manufacturing of OECTs using hybrid inkjet-FDM techniques and commercially available printing material can be achieved. The fabricated devices show good transistor and neuromorphic performances, validating the use of the additive manufacturing as a technique to fabricate neuromorphic OECTs. A second study is conducted to understand the effects of design geometries and print parameters on the electrical properties of inkjet-FDM printed materials and transistor characteristics. The findings show that design geometries and parameters strongly influence electrical parameters, giving in-depth understanding of the design considerations needed when designing and extruding material for OECT fabrication. Finally, a neuromorphic neural network is designed and fabricated using the hybrid inkjet-FDM process. The neural network is in the form of a crossbar array with global electrolyte gating. The fabricated devices show transistor and neuromorphic neural network characteristics, illustrating the feasibility and opportunity that digital manufacturing offers in the field of OECT technology.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2022

Author dc:creator

• Mangoma, Tanyaradzwa

Advisor dc:contributor.advisor

• Daly, Ronan

• additive manufacturing
• bioelectronics
• neuromorphic computing

Licence

• https://www.repository.cam.ac.uk/bitstreams/83b79f91-dc2b-4fd2-95de-9d12a6932d81/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.97018

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/350561