Damascene versus subtractive line CMP process for resistive memory crossbars BEOL integration
Bibliographic record
Abstract
In recent years, resistive memories have emerged as a pivotal advancement in the realm of electronics, offering numerous advantages in terms of energy efficiency, scalability, and non-volatility [1]. Characterized by their unique resistive switching behavior, these memories are well-suited for a variety of applications, ranging from high-density data storage to neuromorphic computing [2]. Their potential is further enhanced by their compatibility with advanced semiconductor processes, enabling seamless integration into modern electronic circuits [3]. A particularly promising avenue for resistive memory lies in its integration at the Back-End-of-Line (BEOL) stage of semiconductor manufacturing [4]. BEOL integration involves processes that occur after the fabrication of the transistors, primarily focusing on creating interconnections that electrically link these transistors. Integrating resistive memories at this stage can lead to compact, efficient, and high-performance architectures, pivotal for in-memory computing applications where data storage and processing are co-located [5]. This paper studies three ways to integrate TiOx-based resistive memory into passive crossbar array structures, using chemical mechanical polishing (CMP) processes, focusing on identifying the optimal integration techniques.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".