Leveraging Convolutional Autoencoders for Post-Layout Performance Estimation of Analog ICs
Bibliographic record
Abstract
State-Of-the-art layout-aware synthesis methods for analog integrated circuits (ICs) rely extensively on off-the-shelf layout extractions and post-layout simulations to assess the circuits’ functional behavior, incurring prohibitive optimization time. To address this challenge, this paper proposes the development of a novel post-layout performance regressor based on deep learning (DL) models. Specifically, convolutional variational autoencoders (CVAEs) are applied for unsupervised feature extraction from analog layouts, producing a latent space. Then, a collection of artificial neural networks (ANNs) conducts performance estimation directly from the lower-dimensional space. By the usage of convolutional layers to deal with analog IC layouts, the model learns the underlying impact of the floorplan and interconnects on the functional behavior of the circuit. Preliminary results reveal mean absolute percentage errors (MAPEs) below 2% for different performance metrics of a typical analog structure, with the model inference requiring only 3.9 milliseconds, about 3,000× faster than the full parasitic extraction and simulation.
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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".