Enhancement-mode BEOL In <sub>2</sub> O <sub>3</sub> FETs with Record Logic Performance: Experiments and Compact Modeling
Bibliographic record
Abstract
We demonstrate record logic performance in back-end-of-the-line (BEOL)-compatible enhancement-mode (Emode) amorphous oxide semiconductor (AOS) field-effect transistors (FETs). These devices exhibit near-ideal scalability down to 40 nm in channel length (Lch). Using an In2O3channel by plasma-enhanced atomic-layer deposition (PEALD), we achieve E-mode operation in Lch= 40 nm devices with a maximum drive current (Imax) of 1.35 mA/µm, a peak transconductance (gm,peak) of 490 µS/µm, and a close-to-thermal-limit average room-temperature subthreshold swing (Savg) of 63 mV/dec, all at a drain-to-source voltage (Vds) of 0.5 V. A total source (S) and drain (D) resistance (Rsd) of 169 Ω.µm, record-low among E-mode AOS-FETs, is demonstrated. Capacitance-voltage (C-V) characteristics reveal different threshold voltages (Vt) in the intrinsic channel and in the S/D to gate (G) overlap regions. We develop a physics-based MVS-AOS model which accurately captures the essential physics at play in our experimental devices, including C with frequency dispersion in the S/D region. This work advances state-of-the-art BEOL AOS-FET technology, and paves the way for future design-technology co-optimization (DTCO) on this promising device platform for BEOL monolithic 3D integration.
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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 | 0.001 |
| Open science | 0.001 | 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".