This study presents a model of a fully connected neural network (NN) implemented on elements of integrated photonics, including the software implementation of this architecture. The main purpose was to compare the results of two NNs, for this reason the average correlation coefficient was calculated, which amounted to 0.9904. This indicates a high degree of similarity and accuracy between the model's performance on PC and photonics elements.
This article analyzes the influence of the accuracy of manufacturing microring resonators on the characteristics of sensors based on silicon-on-insulator and silicon nitride platforms of integrated photonics. We estimated a deviation of 8 nm in increasing and decreasing the waveguide width. The results indicate that inaccuracies in the width of the waveguides lead to a resonant shift, but they do not affect the sensor’s sensitivity.
The article describes an approach to simulating a microring resonator structure on the silicon nitride integrated photonics platform when exposed to various hazardous to human health gases. We simulated various gases by changing the refractive index of the medium surrounding the resonator from 1 (vacuum) to 1.001768 (CCl4). The microring structure resonant wavelengths varied for various gases, and the quality factor, sensitivity, and intrinsic detection limit were determined. The simulation results show that it is possible to detect a wide range of gases hazardous to human health, including carbon tetrachloride, mercury vapor, carbon monoxide, and nitrogen monoxide, using the developed sensor. However, it is impossible to distinguish the last two gases based on the results of the current work using the SiN platform. Coatings are one of the potential ways to improve the designed sensor for detecting these gases.
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