Terahertz Radiation Detectors Using CMOS Compatible SOI Substrates
Bibliographic record
Abstract
Abstract In recent years, silicon‐based room temperature Terahertz (THz) detectors have become the most optimistic research area because of their high speed, low cost, and unimpeded compatibility with mainstream complementary metal‐oxide‐semiconductor (CMOS) device technologies. However, Silicon (Si) suffers from low responsivity and high noise at THz frequencies. In this review, the recent advances in Si‐based THz detectors using silicon‐on‐insulator (SOI) substrates are presented. These offer several advantages over bulk counterparts, such as reduced parasitic capacitance, enhanced electric field confinement, and improved thermal isolation. The different types of THz detectors exploiting SOI substrate, such as conventional metal‐oxide‐semiconductor field effect transistors (MOSFETs), junction‐less MOSFETs, junction‐less nanowires field effect transistors (JLNWFETs), micro‐electromechanical system (MEMS), metal‐semiconductor‐metal (MSM) structures, and single electron transistor (SET), are discussed, and their key performances in terms of responsivity, noise equivalent power (NEP), bandwidth, and dynamic range are compared. The challenges and opportunities for further improvement of SOI THz detectors, such as device scaling, integration, and modulation, are also highlighted. This review may offer compelling evidence supporting the idea that SOI THz detectors have the potential to facilitate high performance, low power consumption, and scalability—qualities essential for advancing next‐level technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".