Deep Learning Enabled Low Cost Photonic Sensors

虽然光子传感器的研究在过去十年中取得了巨大的进步，并且在实验室中得到了广泛的应用，但其在实际应用中仍然受到测试设备和光子芯片成本高的限制。该研究计划旨在利用纳米光子学和集成边缘人工智能（edge-AI）开发低成本光学和光子学传感器。虽然考虑的主要应用是食品质量传感器，但该提案旨在开发一个集成平台，在医疗保健和国防等其他领域应用。据估计，全世界每年约有13亿吨食物被浪费，造成超过1万亿美元的损失。除了财务影响，还有非常显著的环境影响。浪费的食物导致了用于耕种的化肥和杀虫剂等化学物质的浪费；运输中使用的废弃燃料；以及腐烂产生的甲烷。在加拿大，有机垃圾是垃圾填埋场的第二大组成部分。如果通过动态路由更好地管理食物，大多数食物浪费是可以避免的。然而，这需要在分销和零售中心测量食品质量和保质期的能力。可见和近红外光谱已经显示出前景，但光谱仪的高成本使得解决方案不可行。在最近的工作中，我们已经证明，精心选择的波长可以用来测量保质期和质量指标，几乎和光谱仪一样精确，同时降低了几个数量级的成本。在这部分的研究计划中，我们将研究设计和制造低成本的应用专用集成光谱仪，并使用边缘人工智能来校正光学像差和化学计量学。具体来说，我们将研究基于纳米线的光子集成成像仪，它允许在同一元件内进行光谱滤波和检测。商业部署面临的一个主要问题是模型的复杂训练，其中必须尝试许多算法组合以找到最优化的模型。最近，我们开发了一个深度学习模型，它将deep - q强化学习与监督学习相结合。该方法允许在标准监督学习所需时间的2- 3%的时间内训练AI模型。在这个项目中，我们将进一步发展这个模型，并将其与一种新的深度学习算法相结合，通过不同的模型组合，找到最优的模型。在5年的短期内，我们将通过原料的化学计量学来开发用于预测高温加工中丙烯酰胺形成的传感器。从长远来看，我们将扩展该技术以构建多光谱成像仪并研究更多应用。该研究项目旨在利用我们团队在纳米光子学、纳米制造和智能手机集成光学传感器领域取得的进展。

While photonic sensors have made tremendous progress in last decade in research and are commonly used in laboratories, their use in field is still hampered by the high cost of testing equipment and photonics chips. The research program aims to develop low-cost optics and photonics sensors using nanophotonics and integrating edge-artificial intelligence (edge-AI). While the main application considered is sensors for food quality, the proposal aims to develop an integrated platform with applications in other areas including healthcare and defense. Worldwide it is estimated that approximately 1.3 billion tons of food is wasted annually resulting in a loss of over $1 Trillion dollars. Besides the financial impact, there is also very significant environmental impact. The wasted food leads to wasted chemicals like fertilizers and pesticides used to cultivate; wasted fuel used in transport; and the creation of methane from decay. In Canada, organic waste is the second highest component of the landfill. Most of this food waste is avoidable if the food was better managed through dynamic routing. However, that requires the ability to measure the quality and shelf life of food at distribution and retail centers. Visible and near-infrared spectroscopy has shown promise but the high cost of spectrometers makes the solution nonviable. In recent works, we have shown that carefully selected wavelengths can be used to measure shelf life and quality metrics nearly as accurately as spectrometers while reducing the cost by orders of magnitude. In this part of the research program, we will investigate designing and fabricating low cost application specific integrated spectrometer and use of edge-AI to correct for optical aberrations and also chemometrics. Specifically, we will investigate nanowire based photonic integrated imagers which allow spectral filtering and detection within the same element. One major issue facing commercial deployment is the complex training of models where one has to try out many combinations of algorithms to find the most optimized model. Recently, we have developed a deep learning model which integrates Deep-Q reinforcement learning with supervised learning. The method allows to train an AI model in 2-3 % of the time required for standard supervised learning. In this program, we will further develop this model and integrate it with a novel deep learning algorithm which goes through the different combination of models and finds the optimized model. In short term over the 5 years of the proposal, we will develop the sensor for predicting acrylamide formation in high temperature processing through chemometrics of raw materials. In long term, we will extend the technology to build multispectral imagers and investigate more applications. The research program is designed to leverage the advances made in our group in the area of nanophotonics, nanofabrication and smartphone integrated optical sensors.