Pattern-Aware Intelligence Enables Nondestructive, Rapid Quantification of High-Aspect-Ratio Silicon Etching.

Shuyan He, Lang Chen, Bo Wen, Yufeng Jin, Wei Wang

Abstract read

In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shuyan HeSchool of Integrated Circuits, Peking University, Beijing, China.

Lang ChenSchool of Integrated Circuits, Peking University, Beijing, China.ORCID https://orcid.org/0000-0002-8780-7303

Bo WenSchool of Integrated Circuits, Peking University, Beijing, China.

Yufeng JinSchool of Electronic and Computer Engineering, Peking University, Beijing, China.

Wei WangSchool of Integrated Circuits, Peking University, Beijing, China.

Funding

China Postdoctoral Science Foundation 2025M770514National Key R&D Program of China 2023YFB4404100National Natural Science Foundation of China 52521007National Natural Science Foundation of China 62504006

6 · The paper itself

Abstract

High-aspect-ratio (HAR) structures are widely used in microelectromechanical systems (MEMS), electronic devices, and advanced packaging. Accurate characterization of post-etch microstructural morphology is essential for process control and device reliability. However, existing metrology techniques rely on destructive methods and cannot capture the complete three-dimensional profile. This study proposes a pattern-aware intelligent model that transforms pattern-dependent etching behavior into a physical prior, enabling rapid and minimally destructive reconstruction of HAR structures across pattern scales. A YOLO-Pose-based model is developed to automatically extract subpixel geometric features from scanning electron microscopy images, achieving a quantification accuracy of 95.11% with R

Indexed as

high‐aspect‐ratio structuremicroelectromechanical system fabricationphysics‐informed machine learningvirtual metrology

Identifiers

PMID42474113

DOI10.1002/advs.76723

PMCPMC13383699

What Socratic holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.