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Modeling and optimization of nano-rod plasmonic sensor by adaptive neuro fuzzy inference system (ANFIS)

机译：自适应神经模糊推理系统（ANFIS）纳米棒等离子体传感器的建模与优化

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Predicting the behavior of photonic and Plasmon nanostructures has always been an attractive topic for photonic sciences researchers. One of the methods for estimating the behavior of devices in engineering sciences is adaptive neuro fuzzy inference system. Modeling and designing of photonic and plasmonic nanostructures and their optimization strongly relies on the timing of unstructured electromagnetic response simulations and optimal design requires a high number of simulations and if the optimal answer is reached, other simulation results will be wasted. Deep learning method which has been created by ANFIS networks provides a powerful and efficient device for creating accurate relationship between plasmonic geometric parameters and resonance spectrums. Millions of different nanostructures can be obtained without the need for any costly simulations and it costs only one investment to obtain training data. In this study, we use of deep learning based on ANFIS for predicting optic sensors' answer by using plasmonic nano rods. In addition to prediction, this method can approximate a special answer based on photonic structure. In this study, ANFIS networks, at first, model a 5*5 array of plasmonic nanostructures based on obtained experimental data from optic absorption. In the following, based on obtained model, it designs nanoparticles geometry so that we achieve a relatively narrow absorption spectrum with high sensitivity in relation to change air refractive index. This method can be applied for other similar types of nano-photonic systems which can help to destroy simulation procedure and acceleration photonic sensor design trend.
机译：预测光子和等离子体纳米结构的行为一直是光态科学研究人员的吸引力。估计工程科学中设备行为的方法之一是自适应神经模糊推理系统。光子和等离子体纳米结构的建模和设计以及它们的优化强烈依赖于非结构化电磁响应模拟的时机和最佳设计需要大量的仿真，如果达到最佳答案，则将浪费其他仿真结果。 ANFIS网络已创建的深度学习方法提供了一种强大而有效的设备，用于在等离子体几何参数和共振频谱之间创建准确的关系。可以获得数百万不同的纳米结构而无需任何昂贵的模拟，并且只需一项获得培训数据的投资即可。在这项研究中，我们使用基于ANFIS来预测光学传感器的深度学习通过使用等离子体纳米棒来预测视镜传感器的答案。除了预测外，该方法可以近似基于光子结构的特殊答案。在本研究中，ANFIS网络首先，基于从光学吸收的获得的实验数据模型为5 * 5阵列等离子体纳米结构。在下文中，基于所获得的模型，它设计了纳米颗粒几何形状，使得我们在改变空气折射率方面具有高灵敏度的相对窄的吸收光谱。该方法可以应用于其他类似类型的纳米光子系统，这有助于破坏模拟程序和加速光子传感器设计趋势。

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