10:45 AM - EQ11.06.06
Additive Manufacturing Organic Neuromorphic Devices and Neural Networks
Tanyaradzwa Mangoma1,2,George Malliaras2,Ronan Daly1
Institute for Manufacturing, Unviersity of Cambridge1,University of Cambridge2
Drug discovery has become slow and increasingly expensive. One of the primary causes being the 'better than the Beatles' problem, whereby each new drug discovered needs to be a significant improvement to the back catalogue of approved medicines. As an alternative, researchers have been developing bioelectronic therapeutics which leverage electrical signals originating from the patients to modulate biological activity in a closed feedback loop; examples of these bioelectronic systems extend from treatment of diabetics to neuromodulation devices for mediation of epileptic seizures,. Implementation of these bioelectronics requires precise control and personalisation, leading to increasing interest in smart bioelectronics which are specifically designed to fit the patient in contrast to a one-size-fits-all approach. Smart bioelectronics use specific brain-like neuromorphic circuits which emulate characteristics of biological synapses through the co-location of information storage and processing to directly modulate biological environments. However, to achieve the necessary personalisation to tailor these smart devices to individual patients, a modular and adaptable manufacturing technique should be implemented. Herein, we introduce the use of additive manufacturing as a tool to fabricate and deliver personalised neuromorphic devices. Additive manufacturing has moved from rapid prototyping to being used as a direct digital manufacturing tool for end-use electronic components and devices[6–8]. By using additive manufacturing to fabricate neuromorphic devices, we build components layer-by-layer from mutable CAD models, enabling rapid design change and fabrication of more geometrically complex components [9,10]. This rapid feedback in the development of neuromorphic devices allows for smarter, more complex, and personalised bioelectronics. In this presentation, we demonstrate how accessible, low-cost fuse deposition modelling and inkjet printing additive manufacturing techniques can be used to deliver a wide range of neuromorphic devices and circuit architectures. We go on to show how additive manufacturing design geometries and print parameters can be used to control the electronic properties of the devices as well as the functionality of integrated systems. Finally we demonstrate how additive manufacturing can be used to fabricate complex neuromorphic circuits such as spiking neural networks. We hope the work presented shows that cheap and accessible additive manufacturing techniques can be used to deliver smart and personalised bioelectronics into healthcare.
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