طراحی یک جاذب فراسطحی تراهرتز بر پایه تکنیک یادگیری ماشین

نوع مقاله : علمی-پژوهشی

نویسنده

استادیار، دانشکده فنی و مهندسی، دانشگاه گرمسار، گرمسار، ایران

چکیده

توسعه جاذب‌های فراسطحی راه‌حل‌ بالقوه‌ای برای دستیابی به وزن کم، ضخامت نازک، نرخ جذب مطلوب و ویژگی‌های قابل قبول جذب امواج تراهرتز ، ارائه می‌دهد. به منظور بهینه‌سازی خواص جذب فراسطح‌ها، معمولاً از طیف جذب به عنوان یک معیار ارزیابی مهم استفاده میشود که می‌تواند بسیاری از ویژگی‌های مهم مانند مقدار جذب در فرکانس‌های مختلف را نشان دهد. اما، تحلیل طیف‌های جذب، به تعداد زیادی پارامترهای ساختاری وابسته است که منابع و زمان زیادی را مصرف می‌کند، زیرا جذب موج الکترومغناطیسی شامل فرآیندهای تطبیق امپدانس مختلط و تحریک میدان الکتریکی است. برای پرداختن به این موضوع، این تحقیق یک رویکرد یادگیری ماشین مبتنی بر الگوریتم جنگل تصادفی را برای پیش‌بینی نرخ جذب بر اساس پارامترهای ساختاری پیشنهاد می‌کند و نیاز به شبیه‌سازی عددی و زمان تجزیه و تحلیل طیف را کاهش می‌دهد. با مدل جنگل تصادفی، نرخ جذب با امتیاز R2 بیش از 99/0پیش‌بینی می‌شود. علاوه بر این، طرح جاذب پیشنهادی دارای مزایای نازک بودن، غیرحساس بودن به پلاریزاسیون و با زاویه برخورد نسبتاً پایدار به واسطه تقارن ساختار است. این مطالعه یک رویکرد عملی و موثر برای طراحی سیستم‌های پیچیده مرتبط با انتشار موج الکترومغناطیسی جاذب، بازتاب و انتقال ارائه می‌کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Design of a Terahertz Metasurface Absorber Based on Machine Learning Technique

نویسنده [English]

  • M. M. Fakharian
Faculty of Engineering, University of Garmsar, Garmsar, Iran
چکیده [English]

The development of metasurface absorbers offers a potential solution to achieve low weight, thin thickness, favorable absorption rate, and acceptable terahertz absorption characteristics. In order to optimize the absorption properties of metasurfaces, the absorption spectrum is usually used as an important evaluation criterion, which can show many important characteristics such as the rate of absorption at different frequencies. However, the analysis of absorption spectra related to a large number of variable structural parameters is required when designing the structure, which consumes a lot of resources and time, because electromagnetic wave absorption involves the processes of complex impedance matching and electric field excitation. To address this issue, this study proposes a machine learning approach based on a random forest algorithm to predict absorption rates based on structural parameters, reducing the need for numerical simulation and spectrum analysis time. With the random forest model, the absorption rate is predicted with the R2 score of more than 0.99. In addition, the proposed absorber design has the advantages of being thin, insensitive to polarization and with a relatively stable incident angle, due to the symmetry of the structure. This study presents a practical and effective approach for the design of complex systems related to absorbing, reflecting and transmitting electromagnetic wave propagation.

کلیدواژه‌ها [English]

  • Absorber
  • Metasurface design
  • Terahertz
  • Machine learning
  • Random forest algorithm

مراجع

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