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

用于边缘计算的下一代灵活混合传感平台：制造、集成和部署

• 批准号: RGPIN-2022-03083
• 负责人: Cloutier, Sylvain
• 金额: $ 4.01万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753994
• 关键词: Next; generations; flexible; hybrid; sensing

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.

这个雄心勃勃的发现计划的主要目的是使高性能完全集成的柔性混合电子（FHE）传感平台用于边缘计算。这项对超低成本材料和设备的制造，整合和部署的基本研究对于提供新的，更好的无处不在感应和数据处理平台至关重要。 该程序在我团队在使用新功能材料，尖端的数字喷墨打印和新兴的后处理和组装技术的独特的光电设备体系结构中的独特专业和往绩上建立了基础。利用这项基础研究仍然是至关重要的，以最终看到那些令人兴奋的新型超低成本材料和设备产生可行的产品。 同时，我们还希望探索另一种方法，以使用机器学习中的最新突破进一步增强印刷传感器设备的物理性能。确实，在过去几个月中获得的非常初步的结果已经表明，使用低成本印刷传感器以及现有的机器学习技术可以进行巨大的准确性提高。反过来，我们坚信，将更多传统的传感器设计与芯片机器学习能力相结合以在当地运行推断也可以提供更好的数据集，同时减轻与云计算相关的一些延迟和安全问题。 因此，这项雄心勃勃的多方面研究计划的结果将是为科学界和加拿大行业提供新知识和必要的工具，以满足针对广泛应用程序的传感设备的成倍增长需求，包括可穿戴设备和健康监控，高级制造，iot＆智能建筑平台。反过来，这些新的传感平台将有助于提供更完整和更高质量的数据集，以利用新的机器学习和人工智能工具来充分功能。