Dry etching strategy of spin-transfer-torque magnetic random access memory: A review
Bibliographic record
Abstract
The spin-based memory, spin transfer torque-magnetic random access memory (STT-MRAM), has the potential to enhance the power efficiency of high density memory systems. Its desirable characteristics include nonvolatility, fast operation, and long endurance. However, dry etching of MRAM structures remains a challenge as the industry is ramping up its production. In this paper, we explore the etching strategies that have been used to etch the MRAM structures. Several etching techniques have been developed to attain optimal device performance. These are reactive ion etching, time modulated plasma etching, atomic layer etching, and ion beam etching. Sidewall profile, sidewall contamination or damage, redeposition, selectivity, and noncorrosiveness are the main factors to consider while selecting the best etching methods. This paper starts with the fundamentals of MRAM reading, writing, and storing principles and finishes with the current approaches to solve the etch challenges. For etching, the most commonly used magnetic materials such as CoFeB, CoFe, and NiFe are covered in this article.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".