用于卷对卷印刷传感器加速测试的物理信息机器学习

S. Chandra Mouli, S. Sedaghat, Muhammed Ramazan Oduncu, Ajanta Saha, R. Rahimi, Muhammad A. Alam, Alexander Wei, A. Shakouri, Bruno Ribeiro
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引用次数: 0

摘要

卷对卷印刷大大缩短了传感器和物联网设备从设计到生产的时间,同时具有大规模生产的成本效益。但由于可用的制造公差控制较少,传感器厚度、组成、粗糙度等特性无法精确控制。由于这些特性可能会影响传感器的性能,因此卷对卷印刷传感器在部署到现场之前需要进行验证测试。在这项工作中,我们改进了需要在已知硝酸盐浓度的溶液中校准约1-2天的硝酸盐传感器的测试。为了加速这一过程,我们观察传感器的初始行为几个小时,并使用物理知识的机器学习方法来预测未来24小时的测量结果,从而节省宝贵的时间和测试资源。由于卷对卷印刷的可变性,这项预测任务需要对新测试传感器特性变化具有鲁棒性的模型。我们证明了现有的方法在这项任务中失败,并描述了一种基于物理的机器学习方法,该方法提高了对不同测试条件的预测鲁棒性(与当前最先进的基于物理的机器学习方法相比,在真实数据中降低≈1.7倍,在合成数据中降低≈5倍)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Physics-Informed Machine Learning for Accelerated Testing of Roll-to-Roll Printed Sensors
Roll-to-roll printing has significantly shortened the time from design to production of sensors and IoT devices, while being cost-effective for mass production. But due to less manufacturing tolerance controls available, properties such as sensor thickness, composition, roughness, etc., cannot be precisely controlled. Since these properties likely affect the sensor behavior, roll-to-roll printed sensors require validation testing before they can be deployed in the field. In this work, we improve the testing of Nitrate sensors that need to be calibrated in a solution of known Nitrate concentration for around 1–2 days. To accelerate this process, we observe the initial behavior of the sensors for a few hours, and use a physics-informed machine learning method to predict their measurements 24 hours in the future, thus saving valuable time and testing resources. Due to the variability in roll-to-roll printing, this prediction task requires models that are robust to changes in properties of the new test sensors. We show that existing methods fail at this task and describe a physics-informed machine learning method that improves the prediction robustness to different testing conditions (≈ 1.7× lower in real-world data and ≈ 5× lower in synthetic data when compared with the current state-of-the-art physics-informed machine learning method).
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