A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.

提出了一种基于具有二维和三维几何形状的光子晶体结构的温度传感器，并使用机器学习技术评估其测量性能。通过数学建模研究了光子晶体结构的温度特性。基于衬底（硅）和柱（空气）材料对 1200 nm 信号的有效介电常数来研究该结构的物理特性，而使用平面波扩展方法研究其操作的数学原理。此外，根据此类光子结构经常考虑的吸收和反射损失来研究固有特性。通过结构的输出信号（传输能量）决定了相应温度变化的幅度。此外，数值解释表明，对于二维和三维光子结构，输出信号随温度呈非线性变化。通过基于多项式回归的机器学习技术发现传输能量与温度之间的关系。此外，严格的数学计算表明，二阶多项式回归可能是建立这种关系的合适候选者。多项式回归是使用 Google Colab 平台上的 Numpy 和 Scikit-learn 库实现的。数值解释表明，对于二维和三维光子结构，输出信号随温度呈非线性变化。通过基于多项式回归的机器学习技术发现传输能量与温度之间的关系。此外，严格的数学计算表明，二阶多项式回归可能是建立这种关系的合适候选者。多项式回归是使用 Google Colab 平台上的 Numpy 和 Scikit-learn 库实现的。数值解释表明，对于二维和三维光子结构，输出信号随温度呈非线性变化。通过基于多项式回归的机器学习技术发现传输能量与温度之间的关系。此外，严格的数学计算表明，二阶多项式回归可能是建立这种关系的合适候选者。多项式回归是使用 Google Colab 平台上的 Numpy 和 Scikit-learn 库实现的。严格的数学计算表明，二阶多项式回归可能是建立这种关系的合适候选者。多项式回归是使用 Google Colab 平台上的 Numpy 和 Scikit-learn 库实现的。严格的数学计算表明，二阶多项式回归可能是建立这种关系的合适候选者。多项式回归是使用 Google Colab 平台上的 Numpy 和 Scikit-learn 库实现的。