论文标题
通过单光子级的机器学习对多参数传感器的强大校准
Robust calibration of multiparameter sensors via machine learning at the single-photon level
论文作者
论文摘要
传感器的校准是验证其操作的基本步骤。这可能是一项艰巨的任务，因为它依赖于获取设备的详细建模，这可能会因其对多个参数的可能依赖性而加剧。机器学习为此问题提供了方便的解决方案，在参数和设备响应之间操作映射，而无需有关其功能的其他特定信息。在这里，我们证明了基于神经网络的算法的应用，取决于两个参数，用于集成光子设备的校准。我们表明，可以通过仔细选择适当的网络培训策略来实现可靠的表征。这些结果表明，这种方法是作为具有复杂转导函数的传感器的多参数校准的有效工具。
Calibration of sensors is a fundamental step to validate their operation. This can be a demanding task, as it relies on acquiring a detailed modelling of the device, aggravated by its possible dependence upon multiple parameters. Machine learning provides a handy solution to this issue, operating a mapping between the parameters and the device response, without needing additional specific information on its functioning. Here we demonstrate the application of a Neural Network based algorithm for the calibration of integrated photonic devices depending on two parameters. We show that a reliable characterization is achievable by carefully selecting an appropriate network training strategy. These results show the viability of this approach as an effective tool for the multiparameter calibration of sensors characterized by complex transduction functions.
