Superior terahertz radiation detection through novel micro circular log-periodic antenna engineered with an advanced evolutionary neural network algorithm
Bibliographic record
Abstract
In this work, we introduce a novel Micro Circular Log-Periodic Antenna (MCLPA) optimized with an advanced Evolutionary Neural Network (ENN) algorithm, specifically designed to enhance terahertz (THz) radiation detection. By leveraging the adaptive capabilities of the ENN framework, the antenna design efficiency is significantly improved, enabling rapid prototyping and yielding highly optimized structures tailored for practical THz applications. Extensive characterization confirms that the proposed MCLPA achieves outstanding performance, including an ultra-broad operational bandwidth of 372 GHz (0.135–0.507 THz), a peak gain of 5.51 dBi, an optimal S-parameter (S11) of −13.68 dB, and a maximum radiation efficiency of 82.39%. In addition, the MCLPA exhibits superior sensitivity, low noise susceptibility, and fast response, which are key attributes for reliable and precise THz detection. When configured in array form, the design further enhances gain and directional responsiveness, demonstrating the scalability and deployment potential of the MCLPA. This ENN-driven MCLPA represents a significant breakthrough in THz antenna engineering, introducing a transformative design paradigm that synergistically integrates algorithmic intelligence with structural innovation. By substantially reducing design time and cost while achieving exceptional performance, the proposed ENN framework sets a new benchmark for the development of next-generation THz detection and communication systems, offering broad implications for future high-frequency technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".