SBIR Phase I: Machine Learning Enhanced Hardware for Optimization and Self-tuning of High-Finesse Optical Cavities

SBIR 第一阶段：用于优化和自调节高精细光腔的机器学习增强硬件

• 批准号: 2025905
• 负责人: Michelle Fritz
• 金额: $ 25.56万
• 依托单位: QUNNECT, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2025905&HistoricalAwards=false
• 关键词: SBIR; Phase; Machine; Learning; Enhanced

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to demonstrate the utility of powerful computational techniques, like machine learning, for auto-alignment of sophisticated optical instruments. This is particularly important for instruments deployed in the field, where environmental fluctuations continuously perturb components in unpredictable ways. Successful implementation of such technologies would allow complex lasers and instruments to transition from the laboratory to commercial use. This could be particularly impactful in the growing quantum communication industry. This project will advance the development of machine learning to align these sensitive instruments autonomously. This Small Business Innovation Research Phase I project will investigate the use of machine learning to maintain alignment of exquisite filters made from optical cavities. To date, no commercial solution exists for auto-alignment of these cavities, which are common components in complex optical instruments. This is due to the complexity of the parameter sets governing alignment, and the challenge to train the system to choose the optimal path to restore alignment. The primary deliverables of this project will be a set of modular machine learning-based optimization algorithms and a commercial off-the-shelf 30MHz-bandwidth self-tuning optical filter hardware module. Since the proposed adaptive control strategy does not rely on a single underlying model, the developed framework will be modular and easily transferable to other nonlinear systems, which depend on photonic arrangements with varying parameters and control features.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛影响将是展示强大的计算技术（如机器学习）在精密光学仪器自动对准中的实用性。这对于部署在现场的仪器尤其重要，因为环境波动会以不可预测的方式不断干扰部件。这些技术的成功实施将使复杂的激光器和仪器从实验室过渡到商业用途。这在不断增长的量子通信行业中可能特别有影响力。该项目将推动机器学习的发展，以自主调整这些敏感仪器。 这个小型企业创新研究第一阶段项目将研究使用机器学习来保持由光学腔制成的精致滤波器的对准。到目前为止，还没有商业解决方案用于这些腔的自动对准，这些腔是复杂光学仪器中的常见组件。这是由于管理对齐的参数集的复杂性，以及训练系统选择最佳路径以恢复对齐的挑战。该项目的主要交付成果将是一套基于模块化机器学习的优化算法和一个商用的30MHz带宽自调谐光学滤波器硬件模块。由于提出的自适应控制策略不依赖于一个单一的基础模型，开发的框架将是模块化的，很容易转移到其他非线性系统，这取决于光子安排与不同的参数和控制feature.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。