Dual‐Gated MoS<sub>2</sub> Memtransistor Crossbar Array
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Memristive systems offer biomimetic functions that are being actively explored for energy‐efficient neuromorphic circuits. In addition to providing ultimate geometric scaling limits, 2D semiconductors enable unique gate‐tunable responses including the recent realization of hybrid memristor and transistor devices known as memtransistors. In particular, monolayer MoS 2 memtransistors exhibit nonvolatile memristive switching where the resistance of each state is modulated by a gate terminal. Here, further control over the memtransistor neuromorphic response through the introduction of a second gate terminal is gained. The resulting dual‐gated memtransistors allow tunability over the learning rate for non‐Hebbian training where the long‐term potentiation and depression synaptic behavior is dictated by gate biases during the reading and writing processes. Furthermore, the electrostatic control provided by dual gates provides a compact solution to the sneak current problem in traditional memristor crossbar arrays. In this manner, dual gating facilitates the full utilization and integration of memtransistor functionality in highly scaled crossbar circuits. Furthermore, the tunability of long‐term potentiation yields improved linearity and symmetry of weight update rules that are utilized in simulated artificial neural networks to achieve a 94% recognition rate of hand‐written digits.
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.
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 | 0.000 | 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) | 0.000 | 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