28 nm FD-SOI embedded phase change memory exhibiting near-zero drift at 12 K for cryogenic spiking neural networks (SNNs)
Why this work is in the frame
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Bibliographic record
Abstract
<title>Abstract</title> 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it