Improved On-Resistance of Cryogenic LDMOS Devices Utilizing a Field Plate for Scaling Up Trapped Ion Quantum Computers
Bibliographic record
Abstract
Integrated cryogenic analog multiplexers are essential for scaling up trapped ion quantum computers (TIQC). They require high-voltage devices, such as LDMOS, to operate within the required voltage range of upto ±40 V. However, at cryogenic temperatures, below 40 K, LDMOS devices exhibit a diode-like behavior, leading to nonlinear on-resistance at low VDS. This negatively affects settling time and transfer function linearity of the required analog multiplexers. Previous work on cryogenic LDMOS focused on characterizing and modeling their behavior. This work explores the use of a field plate to improve the on-resistance performance of LDMOS devices at cryogenic temperatures. Our field plate engineered device was fabricated and measured at cryogenic temperatures alongside a baseline device. Our key findings include: 1) a field plate is essential at cryogenic temperatures; 2) it reduces on-resistance by six orders of magnitude at low VDS; 3) it degrades the breakdown voltage; 4) device nonlinear region span decreases to 40 mV compared to the previous threshold of 400 mV. Finally, TCAD-based cryogenic simulations demonstrate that the remaining threshold stems from an energy barrier in the conduction band at STI corners. These results show for the first time that regaining the linear behavior of LDMOS devices’ on-resistance at cryogenic temperatures can be achieved, which is essential to scale up the number of qubits and hence bring quantum computers to commercial use.
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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 | 0.001 | 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".