Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.083413
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Enabling Bias-Dependent Electronic Morphology Analysis of Single Molecules in STM via Deep Segmentation with Noise-Aware Calibration

Lingtao Zhan1, Jiale Zhu1, Tingting Wang1, Xiongbai Cao1, Xiaoyu Hao1,2, Cesare Grazioli3, Quanzheng Zhang1, Huixia Yang1, Teng Zhang1,*, Yeliang Wang1
1 School of Integrated Circuits and Electronics & Yangtze Delta Region Academy, Beijing Institute of Technology (BIT), Beijing, China
2 Institute of Quantum Materials and Physics, Henan Academy of Sciences, Henan, China
3 CNR-IOM, Istituto Officina dei Materiali, Basovizza SS-14, Km 163.5, Trieste, Italy
* Corresponding Author: Teng Zhang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.083413

Received 03 April 2026; Accepted 23 July 2026; Published online 28 August 2026

Abstract

Scanning tunneling microscopy (STM) images are frequently affected by low-frequency vibrations, substrate-induced background variations, and bias-dependent contrast changes, which degrade molecular feature responses and hinder reliable segmentation. To address this challenge, we develop an STM-oriented Feature Pyramid Network (FPN) + Dual-Path Intensity Calibration (DPIC) framework by adapting a DPIC module, originally derived from a cloud-noise calibration mechanism, to the specific characteristics of STM molecular images. In this framework, DPIC is reformulated as a noise-aware feature calibration module that suppresses low-response background interference while preserving foreground molecular contours. We integrated DPIC into the FPN architecture and systematically evaluated the model on an in-house STM molecular image dataset. Expanded baseline comparisons and image-level five-fold cross-validation were further conducted to assess segmentation performance and stability. The proposed FPN + DPIC model achieved an Intersection over Union (IoU) of 0.8347 ± 0.0073 and a Dice score of 0.9077 ± 0.0050, outperforming representative deep learning, traditional segmentation, and scanning tunneling microscopy (STM)-related comparison methods under the same evaluation protocol. Furthermore, we applied the model to bias-dependent STM images of single 4,4′-Methylenebis(N,N-diphenylaniline) (MTDATA) molecules. By introducing Radial Skewness as a contour-derived geometric descriptor, we quantified the bias-dependent evolution of molecular electronic-state morphology. Statistical correlation analysis further showed that Radial Skewness is negatively associated with the Highest Occupied Molecular Orbital (HOMO)-related spectral response within the measured STM/scanning tunneling spectroscopy (STS) series. This work provides a noise-aware segmentation strategy for the tested MTDATA STM molecular images and demonstrates the potential of segmentation-assisted morphology analysis for quantitative interpretation of bias-dependent STM/STS data under the investigated molecular system and imaging conditions.

Keywords

Semantic segmentation; scanning tunneling microscopy (STM); noise calibration; electronic morphology; optoelectronic materials

  • 22

    View

  • 5

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link