<scp>Long‐term</scp> and s<scp>hort‐term</scp> plasticity independently mimicked in highly reliable <scp>Ru‐doped Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub></scp> electronic synapses
Bibliographic record
Abstract
Abstract In order to fulfill the complex cognitive behaviors in neuromorphic systems with reduced peripheral circuits, the reliable electronic synapses mimicked by single device that achieves diverse long‐term and short‐term plasticity are essential. Phase change random access memory (PCRAM) is of great potential for artificial synapses, which faces, however, difficulty to realize short‐term plasticity due to the long‐lasting resistance drift. This work reports the ruthenium‐doped Ge2Sb2Te5 (RuGST) based PCRAM, demonstrating a series of synaptic behaviors of short‐term potentiation, pair‐pulse facilitation, long‐term depression, and short‐term plasticity in the same single device. The optimized RuGST electronic synapse with the high transformation temperature of hexagonal phase >380°C, the outstanding endurance >108 cycles, the low resistance drift factor of 0.092, as well as the extremely high linearity with correlation coefficients of 0.999 and 0.976 in parts of potentiation and depression. Further investigations also go insight to mechanisms of Ru doping according to thorough microstructure characterization, revealing that Ru dopant is able to enter GST lattices thus changing and stabilizing atomic arrangement of GST. This leads to the short‐term plasticity realized by RuGST PCRAM. Eventually, the proposed RuGST electronic synapses performs a high accuracy of ~94.1% in a task of image recognition of CIFAR‐100 database using ResNet 101. This work promotes the development of PCRAM platforms for large‐scale neuromorphic systems.
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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) | 0.001 | 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".