Bifacially Engineered Perovskite‐Based Synaptic Memristors Achieve High Linearity and Symmetricity for Accurate and Robust Neuromorphic Computing
Bibliographic record
Abstract
Abstract Achieving both high linearity and symmetricity in metal halide perovskite (MHP)‐based memristors remains challenging, primarily due to their abrupt switching behaviors and irregular conductive filament (CF) pathways. Here, bifacially engineered MHP memristors exhibiting simultaneous high linearity, symmetricity, and reliability are reported. Top‐surface passivation using phenylethylammonium iodide (PEAI) facilitates the formation of an ultrathin 2D perovskite layer (PEA 2 PbI 4 ), promoting gradual switching and effectively suppressing ion migration during CF formation, thereby significantly enhancing the linearity of long‐term potentiation. Meanwhile, bottom‐side PEAI treatment alleviates tensile strain and enhances perovskite grain uniformity, leading to stable CF rupture and improved linearity in long‐term depression as well as symmetricity. The resulting bifacially engineered memristor device achieves an exceptionally high I on / I off ratio of 3.67 × 10 5 , remarkable endurance exceeding 11 000 cycles, and robust data retention time over 10 5 s. Moreover, these bifacially engineered synaptic memristors demonstrate superior classification accuracies of 92.60% and 94.53% in Canadian Institute for Advanced Research 10 (CIFAR‐10) and Modified National Institute of Standards and Technology (MNIST) simulations, respectively. This study provides an effective engineering strategy for overcoming persistent challenges in MHP‐based memristors, thus advancing their potential for next‐generation hardware‐based neuromorphic computing applications.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".