A Parametric DFT Scheme for STT-MRAMs
Bibliographic record
Abstract
Process control and yield of spin torque transfer-magnetoresistive random access memory (STT-MRAM) array are of crucial importance in fabrication. While yield depends on the CMOS process variability, quality of the deposited MTJ film, and other process nonidealities, test platform can enable a parametric optimization and verification process using the CMOS-based design-for-testability (DFT) circuits. In this paper, we develop a DFT algorithm and implement a DFT circuit for parametric testing and prequalification of the critical circuits in the CMOS wafer. The DFT circuit successfully replicates the electrical characteristics of MTJ devices and captures their spatial variation across the wafer with an error of less than 4%. We estimate the yield of the read sensing path by implementing the DFT circuit, which can replicate the resistance-area product variation up to 50% from its nominal value. The yield data from the read sensing path at different wafer locations are analyzed, and a usable wafer radius up to 75 mm has been estimated. Our DFT scheme can provide quantitative feedback based on in-die measurement, enabling fabrication process optimization through iterative estimation and verification of the calibrated parameters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".