28 nm FD-SOI embedded phase change memory exhibiting near-zero drift at 12 K for cryogenic spiking neural networks (SNNs)
Bibliographic record
Abstract
Abstract Seeking to circumvent the bottleneck of conventional computing systems, alternative methods of hardware implementation, whether based on brain-inspired architectures or cryogenic quantum computing systems, invariably suggest the integration of emerging non-volatile memories. However, the lack of maturity, reliability, and cryogenic-compatible memories poses a barrier to the development of such scalable alternative computing solutions. To bridge this gap and outperform traditional CMOS charge-based memories in terms of density and storage, 28 nm Fully Depleted Silicon on Insulator (FD-SOI) substrate-embedded GexSbyTez phase change memories (ePCMs) are characterized down to 12 K. The multi-level resistance programming and its drift over time are investigated. The ePCM can be programmed to achieve and encode 10 different resistance states, at 300 K, 77 K, and 12 K. Interestingly, the drift coefficient is considerably reduced at cryogenic temperatures. Cycle-to-cycle programming variability and resistance drift modelling are carefully used to forecast and evaluate the effect of resistance evolution over time on a fully connected feedforward spiking neural network (SNN) at different temperatures. System-level simulation of a Modified National Institute of Standards and Technology database (MNIST) classification task is performed. The SNN classification accuracy is sustained for up to two years at 77 K and 12 K while a 7–8% drop in accuracy is observed at 300 K. Such results open new horizons for the analogue/multilevel implementation of ePCMs for space and cryogenic applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".