Performance Analysis of Open-Gate Junction FET: A New Foundry-Based Silicon Transistor for Biochemical Sensing Applications
Bibliographic record
Abstract
This paper investigates the performance and parametric design of a new foundry-based silicon field-effect transistor (FET) sensing platform known as the open-gate junction field-effect transistor (OG-JFET) for bio/chemical sensing applications. The fabrication process of the OG-JFET relies on a standard foundry process, requiring the establishment of parametric design rules to understand the effect of crucial sensor performance factors, including transconductance (g$_{\mathrm {m}}$) and device efficiency ($\eta =$gm/I$_{\mathrm {ds}}$). The study examines the impact of various geometric parameters (e.g., channel length and thickness) and material-related parameters (such as boron and phosphorous impurity doping levels) on sensor performance. Simulations provide insights and guidelines for the efficient design and characterization of the OG-JFET, focusing on enhancing gm and maximizing$\eta $for biosensing applications. Experimental measurements of the OG-JFET demonstrate a current range of$\sim ~200~\mu $A/$\mu $m, a high gm of approximately$\sim ~1700~\mu $S ($340~\mu $S/$\mu $m), and$\eta $of$\sim ~4.5$V−1 (for a channel length of$5~\mu $m), which are of importance for circuit design and biosensing application of OG-JFET. These results showcase the performance of this sensor compared to other silicon-based FET platforms for internal signal amplification in sensing applications of OG-JFET. The findings of this paper offer valuable guidelines for the design of sensors based on OG-JFET technology, enabling gaining insights into the impact of sensor structure on sensing performance. Also, we have demonstrated how two performance parameters can be utilized to compare two different designs of OG-JFET, which is useful for future designers.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".