Superior terahertz radiation detection through novel micro circular log-periodic antenna engineered with an advanced...

Area of Science:

• Electromagnetics and Applied Physics
• Antenna Engineering
• Terahertz Technology

Background:

• Terahertz (THz) radiation detection requires highly sensitive and efficient antenna systems.
• Traditional antenna design methods can be time-consuming and may not achieve optimal performance for specific applications.
• The integration of artificial intelligence in electromagnetic design is an emerging area for performance enhancement.

Purpose of the Study:

• To introduce a novel Micro Circular Log-Periodic Antenna (MCLPA) optimized using an advanced Evolutionary Neural Network (ENN) algorithm.
• To enhance the efficiency and performance of antennas for terahertz (THz) radiation detection.
• To demonstrate a new paradigm for antenna design by integrating algorithmic intelligence with structural innovation.

Main Methods:

• Development of a Micro Circular Log-Periodic Antenna (MCLPA) structure.
• Optimization of the MCLPA design using an advanced Evolutionary Neural Network (ENN) algorithm.
• Extensive electromagnetic characterization and performance analysis of the designed antenna.

Main Results:

• The proposed MCLPA achieved an ultra-broad operational bandwidth of 372 GHz (0.135-0.507 THz).
• Key performance metrics include a peak gain of 5.51 dBi, S₁₁ of -13.68 dB, and a radiation efficiency of 82.39%.
• The antenna demonstrated superior sensitivity, low noise susceptibility, and fast response times crucial for THz detection.

Conclusions:

• The ENN-optimized MCLPA offers a significant breakthrough in THz antenna engineering, providing exceptional performance.
• The integration of ENN accelerates design time and reduces costs for developing advanced THz systems.
• This approach sets a new benchmark for next-generation THz detection and communication systems.