Next-generations of flexible hybrid sensing platforms for edge-computing: fabrication, integration and deployment

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The primary objective of this ambitious Discovery program is to enable high-performance fully-integrated flexible hybrid electronic (FHE) sensing platforms for edge-computing. This fundamental research into the fabrication, integration and deployment of ultra low-cost materials and devices is essential to provide new and better ubiquitous sensing and data processing platforms. This program builds on my team's unique expertise and track record in the fabrication and integration of unique optoelectronic device architectures using new functional materials, cutting-edge digital inkjet printing and emerging post-processing & assembly techniques. Leveraging this fundamental research remains absolutely essential in order to eventually see those exciting new ultra low-cost materials and devices spawn viable products. At the same time, we also wish to explore a different approach to further enhance the physical performance of our printed sensor devices using recent breakthroughs in machine learning. Indeed, very preliminary results acquired in the last few months already suggest that dramatic accuracy improvements can be possible using low-cost printed sensors together with existing machine learning techniques. In turn, we firmly believe that combining more conventional sensor designs with on-chip machine learning capabilities to run inferences locally can also provide much better datasets, while alleviating some of the latency and security concerns associated with cloud computing. As such, the outcome of this ambitious multifaceted research program will be to provide the scientific community and the Canadian industry with the new knowledge and necessary tools to meet the exponentially-growing demand for sensing devices targeting a wide range of applications including wearables & health monitoring, advanced manufacturing, IoT & smart building platforms. In turn, these new sensing platforms will help provide more complete and higher-quality datasets in order to exploit new machine learning and artificial intelligence tools to their fullest capabilities.