دوره 18، شماره 2 - ( 9-1403 )
جلد 18 شماره 2 صفحات 288-273 |
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Dorrani Z. Improving the Sensitivity and Stability of Photonic Crystal Temperature Sensors with Deep Learning Autoencoders. IJOP 2024; 18 (2) :273-288
URL: http://ijop.ir/article-1-591-fa.html
URL: http://ijop.ir/article-1-591-fa.html
Improving the Sensitivity and Stability of Photonic Crystal Temperature Sensors with Deep Learning Autoencoders. . 1403; 18 (2) :273-288
چکیده: (37 مشاهده)
Photonic temperature sensors play a crucial role in industrial, medical, and environmental applications. Despite significant progress, challenges such as limited sensitivity, noise, and instability under real-world conditions persist. Most existing sensors rely on non-topological structures, making them vulnerable to structural defects and environmental disturbances with limited temperature operational ranges. Therefore, there is a pressing need for high-sensitivity, stable sensors with wide temperature ranges. In this study, a novel approach based on a one-dimensional topological temperature sensor composed of hyperbolic metamaterial-based photonic structures is presented. The key innovation involves leveraging a deep autoencoder to enhance spectral signal quality and extract robust latent features resistant to noise and defects. This approach optimizes sensor sensitivity and quality, enabling precise temperature prediction over a wide range from 20 to 375 °C. Numerical simulation results demonstrate a sensitivity improvement of up to 0.4 nm/°C, along with outstanding quality performance, achieved through structural optimization and the integration of deep learning. This achievement yields a sensor with high accuracy, exceptional stability, and robustness against environmental perturbations, thereby opening new avenues for applications in both industrial and medical fields.
نوع مطالعه: پژوهشي |
موضوع مقاله:
نانوفوتونیک و ساختارهای نانویی
دریافت: 1404/4/30 | ویرایش نهایی: 1405/6/17 | پذیرش: 1405/7/11
دریافت: 1404/4/30 | ویرایش نهایی: 1405/6/17 | پذیرش: 1405/7/11
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