In recent years, there has been a significant surge in interest surrounding the utilization of machine learning techniques to optimize the performance of fiber optic sensors. This paper delves into the integration of machine learning methodologies, with a specific focus on Explainable Artificial Intelligence (XAI) models, within the domain of fiber optic surface plasmon resonance (SPR) sensors. We implement XAI techniques into a previously-trained Gaussian Process Regression (GPR) model to gain insights into the intricate patterns and features learned by the model, thereby providing valuable insights into its decision-making process. Furthermore, we present a comparative analysis of XAI models, namely and , to elucidate the individual impact of light wavelength (λ) and metal layer thickness (d) on the enhancement of sensor’s figure of merit (FOM). The results obtained from both and methods lead to a common finding that compared to λ, it is the value of d that possesses a significantly greater influence on achieving maximum FOM. Furthermore, this finding is in complete agreement with the concept of radiation damping related to fiber optic SPR sensor structures. This study has the potential to contribute to the refinement of design and fabrication processes related to fiber optic SPR sensor for a wide variety of applications.

中文翻译：

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可解释人工智能在优化光纤SPR传感器性能和设计中的应用

近年来，人们对利用机器学习技术来优化光纤传感器性能的兴趣显着增加。本文深入研究了机器学习方法的集成，特别关注光纤表面等离子共振 (SPR) 传感器领域内的可解释人工智能 (XAI) 模型。我们将 XAI 技术实施到之前训练的高斯过程回归 (GPR) 模型中，以深入了解模型学习的复杂模式和特征，从而为其决策过程提供有价值的见解。此外，我们还对 XAI 模型（即 和 ）进行了比较分析，以阐明光波长 (λ) 和金属层厚度 (d) 对传感器品质因数 (FOM) 增强的单独影响。从这两种方法获得的结果得出一个共同的发现，即与 λ 相比，d 的值对实现最大 FOM 的影响要大得多。此外，这一发现与光纤 SPR 传感器结构相关的辐射阻尼概念完全一致。这项研究有可能有助于完善与光纤 SPR 传感器相关的各种应用的设计和制造工艺。