Generative-Neural-Networks-Based PV-Aware Analog Layout Correction
Bibliographic record
Abstract
A slight deviation from the original pattern in the layout may lead to large parasitic variation and mismatch among devices, which can in turn significantly degrade circuit performance in advanced technology nodes. Mask resolution enhancement operations are highly essential in layout design of analog integrated circuits. In this paper we present an optical proximity correction (OPC) method to serve as a privileged remedy to mask infidelity. Our work is dedicated to taking advantage of the emerging machine learning techniques to boost mask fidelity in analog layout design. We first deploy a predictive gate sizing method to alleviate the process variation (PV) effects in matching transistors. Then, a generative neural network with U-Net is used to perform OPC operations with low computational effort. The experimental results show that our trained network together with our gate sizing method can improve the image fidelity equivalent to the published best OPC methods but 8.43X faster while significantly preserving analog circuit performance by reducing the PV effects.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".