A Physics‐Guided Generative Diffusion Model for Super‐Resolution AFM of Trap Dynamic Behavior

{"Atomic":[0],"force":[1,101],"microscopy":[2,102],"(AFM)":[3],"offers":[4],"nanoscale":[5],"insights":[6],"into":[7],"material":[8,147],"properties,":[9],"yet":[10],"its":[11],"intrinsic":[12],"speed-resolution":[13],"trade-off":[14],"severely":[15],"restricts":[16],"the":[17,45,93,125],"observation":[18],"of":[19,48],"dynamic":[20,134],"processes.":[21],"Although":[22],"deep":[23],"learning-based":[24],"super-resolution":[25],"holds":[26],"promise,":[27],"existing":[28],"models":[29],"often":[30],"fail":[31],"on":[32],"experimental":[33],"data":[34],"due":[35],"to":[36,59,106,131],"a":[37,54,65,71,140],"\\"paradigm":[38],"mismatch\\"":[39],"between":[40],"synthetic":[41],"training":[42],"degradations":[43],"and":[44],"physical":[46],"subsampling":[47],"AFM.":[49],"Here,":[50],"we":[51],"present":[52],"StableAFM,":[53],"physics-guided":[55],"generative":[56,89],"framework":[57],"designed":[58],"break":[60],"this":[61],"barrier.":[62],"By":[63],"integrating":[64],"physically":[66],"consistent":[67],"degradation":[68],"model":[69],"with":[70],"novel":[72],"latent-space":[73],"diffusion":[74],"posterior":[75],"sampling":[76],"strategy,":[77],"our":[78],"approach":[79],"restores":[80],"high-fidelity":[81],"features":[82],"from":[83],"rapid,":[84],"sparse":[85],"scans":[86],"while":[87],"suppressing":[88],"artifacts.":[90],"We":[91],"demonstrate":[92],"method's":[94],"transformative":[95],"potential":[96],"by":[97],"accelerating":[98],"Kelvin":[99],"probe":[100],"(KPFM)":[103],"acquisition":[104],"fourfold":[105],"map":[107],"charge":[108],"dynamics":[109],"in":[110],"multilayer":[111],"ceramic":[112],"capacitors.":[113],"The":[114],"super-resolved":[115],"ISPD":[116],"analysis":[117],"further":[118],"resolves":[119],"an":[120],"annular-like":[121],"depletion":[122],"pattern":[123],"within":[124],"deep-level":[126],"electron-trap":[127],"aggregates.":[128],"This":[129],"ability":[130],"resolve":[132],"complex,":[133],"sub-micron":[135],"structures":[136],"establishes":[137],"StableAFM":[138],"as":[139],"robust,":[141],"high-throughput":[142],"pathway":[143],"for":[144],"advanced":[145],"functional":[146],"characterization.":[148]}

Authors

Institutions

Publication Details

Journal
Small Methods
Published
2026-09-17
DOI
https://doi.org/10.1002/smtd.71042
Primary Topic
Force Microscopy Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

A Physics‐Guided Generative Diffusion Model for Super‐Resolution AFM of Trap Dynamic Behavior

Kaimin Du, Lihuan Liu, Huarong Zeng, Lei Wang et al.
Small Methods
Force Microscopy Techniques and Applications
article

A Physics‐Guided Generative Diffusion Model for Super‐Resolution AFM of Trap Dynamic Behavior

Kaimin Du, Lihuan Liu, Huarong Zeng, Lei Wang, Wentong Du, Kunyu Zhao
article en

Abstract

Atomic force microscopy (AFM) offers nanoscale insights into material properties, yet its intrinsic speed-resolution trade-off severely restricts the observation of dynamic processes. Although deep learning-based super-resolution holds promise, existing models often fail on experimental data due to a "paradigm mismatch" between synthetic training degradations and the physical subsampling of AFM. Here, we present StableAFM, a physics-guided generative framework designed to break this barrier. By integrating a physically consistent degradation model with a novel latent-space diffusion posterior sampling strategy, our approach restores high-fidelity features from rapid, sparse scans while suppressing generative artifacts. We demonstrate the method's transformative potential by accelerating Kelvin probe force microscopy (KPFM) acquisition fourfold to map charge dynamics in multilayer ceramic capacitors. The super-resolved ISPD analysis further resolves an annular-like depletion pattern within the deep-level electron-trap aggregates. This ability to resolve complex, dynamic sub-micron structures establishes StableAFM as a robust, high-throughput pathway for advanced functional material characterization.

Small Methods
Shanghai Institute of Ceramics (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 13%
Force Microscopy Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.