Yield Maximization of Flip-Flop Circuits Based on Deep Neural Network and Polyhedral Estimation of Nonlinear Constraints
Bibliographic record
Abstract
In this paper, we propose a method based on deep neural networks for the statistical design of flip-flops, taking into account nonlinear performance constraints. Flip-flop design and manufacturing are influenced by random variations in the technological process, making deterministic design approaches inadequate for achieving high yields. The conventional yield maximization method using Monte Carlo (MC) simulation is a time-consuming process. Also, for many performance constraints, either there are no analytical formulations or if they exist, they are not sufficiently accurate to be used in circuit optimization. To address these challenges, we approximated the nonlinear constraints with linearized ones (polyhedral approximation) and performed a yield maximization process which was done by developing our first proposed method. Then in the second proposed method, we used deep neural networks to generate precise nonlinear closed-form models for circuit performance metrics and also replaced MC simulation with an analytical yield formula. The combination of these techniques significantly enhances the speed and accuracy of statistical circuit design by employing powerful gradient-based optimization methods that converge quickly to the optimal solution. Experimental results demonstrate that our proposed approach enables the design of circuits with various performance constraints under process variation, and achieves more optimum results with much fewer iterations and less CPU time compared to the conventional simulation-based yield maximization methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".