CMOS-Compatible Protonic Programmable Resistor Based on Phosphosilicate Glass Electrolyte for Analog Deep Learning
Bibliographic record
Abstract
Ion intercalation based programmable resistors have emerged as a potential next-generation technology for analog deep-learning applications. Proton, being the smallest ion, is a very promising candidate to enable devices with high modulation speed, low energy consumption, and enhanced endurance. In this work, we report on the first back-end CMOS-compatible nonvolatile protonic programmable resistor enabled by the integration of phosphosilicate glass (PSG) as the proton solid electrolyte layer. PSG is an outstanding solid electrolyte material that displays both excellent protonic conduction and electronic insulation characteristics. Moreover, it is a well-known material within conventional Si fabrication, which enables precise deposition control and scalability. Our scaled all-solid-state three-terminal devices show desirable modulation characteristics in terms of symmetry, retention, endurance, and energy efficiency. Protonic programmable resistors based on phosphosilicate glass, therefore, represent promising candidates to realize nanoscale analog crossbar processors for monolithic CMOS 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".