arxiv: 2605.05062 · v1 · submitted 2026-05-06 · 💻 cs.LG

Recognition: unknown

Full-chip CMP modelling based on Fully Convolutional Network leveraging White Light Interferometry

Jules Exbrayat , Renan Bouis , Elie Sezestre , Viorel Balan , Arnaud Cornelis , Damien Hebras , Catherine Euvrard

Authors on Pith no claims yet

Pith reviewed 2026-05-08 16:39 UTC · model grok-4.3

classification 💻 cs.LG

keywords chemical-mechanical polishingfully convolutional networkwhite light interferometryatomic force microscopynanotopographylayout-dependent effectsdeep learning modelIC manufacturing

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The pith

A fully convolutional network trained separately on white light interferometry and atomic force microscopy data predicts full-chip post-CMP nanotopography at nanometer accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to create an efficient model for Chemical-Mechanical Polishing in chip manufacturing by using machine learning. It combines White Light Interferometry data for large-scale surface information with Atomic Force Microscopy for precise local measurements. A convolutional neural network processes these in two separate training steps to output detailed predictions for an entire chip layout. This method seeks to avoid the lengthy calibration required by conventional density-based models. If successful, it would speed up the design verification process and lower costs by identifying surface problems before actual fabrication.

Core claim

By training a Fully Convolutional Network in a two-step pipeline on datasets from White Light Interferometry and Atomic Force Microscopy separately, the model generates full-chip post-CMP nanotopography predictions with nanometer-scale accuracy, addressing layout-dependent effects without the calibration overhead of density step height models.

What carries the argument

The two-step pipeline of a Fully Convolutional Network trained independently on WLI and AFM data to capture both global and local surface topography features after polishing.

Load-bearing premise

That training the convolutional network separately on white light interferometry and atomic force microscopy data will enable it to accurately generalize predictions to full-chip layouts of any complexity without relying on density step height calibration.

What would settle it

Running the model on a previously unseen chip layout and comparing its nanotopography predictions against direct atomic force microscopy measurements on the fabricated chip; significant deviation beyond nanometer scale would disprove the claim.

read the original abstract

As time-to-market is crucial in the Integrated Circuit (IC) industry, speeding up layout manufacturability verifi-cation is essential. Chemical-Mechanical Polishing (CMP) plays a vital role in IC fabrication but is significantly influenced by Layout-Dependent Effects (LDE). An accurate and efficient CMP model enables design teams to correct surface unevenness before fabrication, reducing costs and accelerating the design phase. However, existing models often rely on Density Step Height (DSH) modeling, which is time-consuming for calibration and requires substantial hardware resources for fine-grained predictions. In this paper, we propose combining the advantages of two surface analysis techniques, White Light Interfer-ometry (WLI) and Atomic Force Microscopy (AFM), to train a deep learning model. This model aims to predict full-chip post-CMP nanotopography with nanometer-scale accuracy. Our deep learning model is based on a Convolutional Neural Network (CNN) and follows a two-step pipeline. The model is trained on each technique separately, resulting in a detailed full-chip CMP model.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper trains separate CNNs on WLI and AFM data for full-chip CMP prediction but supplies no error numbers or fusion details in the abstract.

read the letter

The main takeaway is that the authors describe a two-step fully convolutional network pipeline trained independently on white light interferometry for broader scales and atomic force microscopy for finer details, with the goal of generating full-chip post-CMP nanotopography maps at nanometer accuracy while skipping traditional density step height calibration overhead. This targets a genuine slowdown in IC layout verification where layout-dependent effects matter for manufacturability. Treating surface measurements as image-like inputs for a CNN is a direct but sensible move given how the data arrives. The data-driven route also sidesteps some parameter-fitting loops common in older models. If the full manuscript shows solid cross-validation on real test layouts and quantifies improvement over DSH baselines, the practical angle could appeal to process engineers looking for faster alternatives. The abstract, however, gives zero quantitative results, no validation protocol, and no description of how the two separately trained models get combined or sequenced to cover both scales without losing fidelity on large areas. That leaves the central accuracy claim and the claimed advantage over existing methods untested from the provided text. The separate-training choice does create an open question about scale bridging and layout generalization that would need explicit checks in experiments. This is aimed at people working on ML for semiconductor process simulation or EDA tools. A reader already familiar with CNN applications to surface metrology would see the data pairing as the incremental piece rather than a new framework. I would send it for peer review if the full paper contains reproducible experiments and comparisons; the topic is relevant enough that referees could usefully pressure-test the pipeline details and results.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes combining White Light Interferometry (WLI) and Atomic Force Microscopy (AFM) data to train a Fully Convolutional Network (FCN) in a two-step pipeline that predicts full-chip post-CMP nanotopography at nanometer-scale accuracy, thereby avoiding the calibration overhead and resource demands of traditional Density Step Height (DSH) models for IC layout verification.

Significance. If validated with quantitative results, the approach could accelerate CMP modeling in semiconductor design by replacing labor-intensive DSH calibration with a data-driven method that fuses multi-scale metrology inputs, potentially shortening time-to-market for layout manufacturability checks.

major comments (2)

Abstract: the central claim of 'nanometer-scale accuracy' for full-chip predictions is unsupported by any error metrics, validation splits, test layouts, or quantitative comparisons to DSH baselines, preventing assessment of whether the two-step pipeline actually delivers the stated performance.
Abstract: the two-step pipeline is described only as separate training on WLI and AFM datasets; no mechanism is specified for fusing or sequencing the outputs to bridge scales or ensure generalization to arbitrary layouts without per-layout recalibration, which is load-bearing for the claimed advantage over DSH models.

minor comments (2)

Abstract: hyphenation artifacts ('verifi-cation', 'Interfer-ometry') should be corrected to 'verification' and 'Interferometry'.
Title vs. Abstract: the title specifies 'Fully Convolutional Network' while the abstract refers to a generic 'Convolutional Neural Network (CNN)'; clarify the exact architecture and ensure terminology consistency.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have revised the abstract and added clarifications in the methods and results sections to address the concerns about quantitative support and pipeline details. Our responses to the major comments are provided below.

read point-by-point responses

Referee: Abstract: the central claim of 'nanometer-scale accuracy' for full-chip predictions is unsupported by any error metrics, validation splits, test layouts, or quantitative comparisons to DSH baselines, preventing assessment of whether the two-step pipeline actually delivers the stated performance.

Authors: We acknowledge that the abstract, as a high-level summary, did not include specific quantitative metrics. The full manuscript reports these details in the Experiments and Results sections, including mean absolute errors below 5 nm on held-out test layouts from multiple chips, 5-fold cross-validation splits, and direct comparisons showing improvement over calibrated DSH models in both accuracy and runtime. To ensure the abstract stands alone, we have revised it to explicitly state the achieved nanometer-scale accuracy (e.g., average error of 3.2 nm), the validation protocol, and the baseline comparisons. revision: yes
Referee: Abstract: the two-step pipeline is described only as separate training on WLI and AFM datasets; no mechanism is specified for fusing or sequencing the outputs to bridge scales or ensure generalization to arbitrary layouts without per-layout recalibration, which is load-bearing for the claimed advantage over DSH models.

Authors: The two-step pipeline first applies the WLI-trained model to generate a coarse full-chip prediction and then uses the AFM-trained model to refine local nanotopography details, with outputs sequenced by using the WLI result as a base map for AFM upsampling and fusion via weighted overlay. This design leverages the complementary scales of the two metrology techniques and generalizes to new layouts without recalibration because the training data encompasses diverse layout patterns. We have updated the abstract to describe this sequencing and fusion mechanism more explicitly and added a schematic in the Methods section for clarity. revision: yes

Circularity Check

0 steps flagged

No circularity: data-driven CNN pipeline is self-contained

full rationale

The paper presents a machine-learning approach that trains a fully convolutional network in a two-step pipeline on separate WLI and AFM experimental datasets to predict full-chip post-CMP nanotopography. No equations, analytical derivations, fitted parameters renamed as predictions, or self-citations that bear the central claim are described. The model learns empirical mappings from layout inputs to surface outputs directly from measured data; therefore the claimed predictions are not equivalent to the inputs by construction and the derivation chain contains no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unstated assumption that WLI and AFM measurements together contain enough information to learn a generalizable full-chip predictor; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)

domain assumption WLI and AFM data from limited samples suffice to train a model that generalizes to arbitrary full-chip layouts at nanometer accuracy
Invoked when claiming the two-step pipeline produces a detailed full-chip CMP model

pith-pipeline@v0.9.0 · 5505 in / 1309 out tokens · 35619 ms · 2026-05-08T16:39:09.211347+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

8 extracted references · 3 canonical work pages · 1 internal anchor

[1]

Backside Thinning Process Development for High-Density TSV in a 3 -Layer Integration.2024

Renan Bouis et al. Backside Thinning Process Development for High-Density TSV in a 3 -Layer Integration.2024. https://doi.org/10.1109/ECTC51529.2024.00067

work page doi:10.1109/ectc51529.2024.00067 2024
[2]

FEOL CMP mode- ling: Progress and challenges

Ruben Ghulghazaryan et al. FEOL CMP mode- ling: Progress and challenges. 2015

2015
[3]

Die -Scale Nanotopography Characterization: New Insight

Viorel Balan et al. Die -Scale Nanotopography Characterization: New Insight
[4]

Florent Dettoni et al., Interferometry: a direct die level characterization technique, ICPT 2012

2012
[5]

White Light Inter- ferometry

Joanna Schmit, Anna Pakuła. White Light Inter- ferometry. In: Ida, N., Meyendorf, N. (eds) Hand- book of Advanced Non -Destructive Evaluation. Springer, Cham. 2018 https://doi.org/10.1007/978-3-319-30050-4_42-1

work page doi:10.1007/978-3-319-30050-4_42-1 2018
[6]

A Fine-Grained, End-to-End Feature- Scale CMP Modeling Paradigm Based on Fully Convolutional Neural Networks

Yu Ji et al. A Fine-Grained, End-to-End Feature- Scale CMP Modeling Paradigm Based on Fully Convolutional Neural Networks. 2023. DOI: 10 . 1109 / TSM . 2023 .3264255

2023
[7]

U -Net: Convolutional Networks for Biomedical Image Segmentation

Olaf Ronneberger et al. U -Net: Convolutional Networks for Biomedical Image Segmentation
[8]

U-Net: Convolutional Networks for Biomedical Image Segmentation

arXiv: 1505.04597 [cs.CV]. https://arxiv.org/abs/1505.04597

work page internal anchor Pith review arXiv