Performance evaluation of three memory sense amplifiers with input offset cancellation
Bibliographic record
Abstract
The input offset in memory sense amplifiers is a critical parameter that contributes to the practical lower limit on the strength of the differential-mode bitline signals that can be sensed reliably. A typical rule of thumb is that random input offsets of up to 40 mV can be expected in sense amplifiers as a result of inevitable device parameter variations. A related rule of thumb is that the bitline signals should be no less than 100 mV to be reliably sensed in the presence of memory array noise, cell charge leakage, and other inevitable error sources, including the input offset of the sense amplifier. A primary cause of input offset are differences between the device parameters of the main, supposedly matched, sensing transistors. We report the results of a simulation study that determined the dependence of the input offset against mismatch in the threshold voltage of the sensing. transistors. Assuming transistor models from a 180 nm CMOS logic technology, we compared the conventional latch-type sense amplifier with three input offset cancelling sense amplifier designs that were proposed by S. Suzuki and M. Hirata (see IEEE J. of Solid-State Circuits, vol.SC-14, no.6, p.1066-70, 1979), T. Furuyama et al. (see IEDM, p.44-7, 1981), and Y. Watanabe et al. (see IEEE J. of Solid-State Circuits, vol.29, no.1, p.9-13, 1994).
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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 | 0.000 |
| 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 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".