Evidence map›Paper›PMID 42643489›Full record

ArticleResearch (Washington, D.C.)2026

Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors.

Robert K A Bennett, Jan-Lucas Uslu, Harmon F Gault, Asir Intisar Khan, Lauren Hoang, Tara Peña, Kathryn Neilson, Young Suh Song, Zhepeng Zhang, Andrew J Mannix and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Robert K A BennettDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0001-7427-8724
Jan-Lucas UsluDepartment of Physics, Stanford University, Stanford, CA 94305, USA.
Harmon F GaultDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Asir Intisar KhanDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Lauren HoangDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0009-0001-7556-8560
Tara PeñaDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Kathryn NeilsonDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0003-1061-2919
Young Suh SongDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0002-5229-1693
Zhepeng ZhangStanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.ORCID https://orcid.org/0000-0002-9870-0720
Andrew J MannixStanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.ORCID https://orcid.org/0000-0003-4788-1506
Eric PopDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.ORCID https://orcid.org/0000-0003-0436-8534

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, and defect profiles) of two-dimensional (2D) transistors from electrical measurements, enabling automated parameter extraction and technology computer-aided design (TCAD) fitting. To facilitate this task, we implement a simple data augmentation and pretraining approach by training a secondary neural network to approximate a physics-based device simulator. This method enables high-quality fits after training the neural network on electrical data generated from physics-based simulations of ~500 devices, a factor >40× fewer than other recent efforts. Consequently, fitting can be achieved by training on physically rigorous TCAD models, including complex geometry, self-consistent transport, and electrostatic effects, and is not limited to computationally inexpensive compact models. We apply our approach to reverse-engineer key parameters from experimental monolayer WS

Identifiers

PMID42643489
PMCPMC13504040

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.