Browsing by Author "Celebi, C."

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A Knowledge-Driven Computer Vision Framework for Automated Atomic Force Microscopy Surface Characterization
(Elsevier Sci Ltd, 2026) Deveci, D. Gemici; Celebi, C.; Barandir, T. Karakoyun; Unverdi, O.
This study presents an innovative analytical framework developed to automate Atomic Force Microscopy (AFM)-based surface characterization. The proposed methodology integrates computer vision (CV) algorithms and machine learning (ML) techniques to overcome the limitations of conventional observer-dependent approaches and visual inspection methods. In the first stage of the two-step data processing pipeline, raw AFM signals were converted into structured datasets, correspondences between images acquired under different loading conditions were identified, and drift effects in both direction and magnitude were predicted using a LightGBM-based machine learning (ML) model to guide subsequent analytical processes. This process establishes a unified coordinate reference across varying force levels, enabling pixel-level comparability of surface maps. In the second stage, the aligned datasets are systematically analyzed through block-based local maxima detection, edge-based contour extraction, morphological filtering, and skeletonization algorithms. In this way, ridge-like surface features are reliably identified and quantitatively evaluated along their axes under varying force conditions. The framework ensures data integrity while enabling high-resolution and reproducible analyzes. Beyond its automation capability, it is distinguished by its integrated, modular architecture, where each component operates sequentially along a unified processing pipeline. The methodology was validated using epitaxial monolayer graphene grown on the C-face of SiC, successfully demonstrating its ability to resolve both geometric and force-dependent mechanical responses. In this regard, the proposed system extends beyond conventional cross-sectional analysis by providing a drift-aware, knowledge-guided compensation mechanism and directionally resolved evaluation, offering a robust, automation-ready infrastructure for nanoscale surface characterization.
Citation - WoS: 1
Comprehensive analysis and machine learning-based solutions for drift behavior in ambient Atomic Force Microscope conditions
(PERGAMON-ELSEVIER SCIENCE LTD, 2025) D. Gemici Deveci; T. Karakoyun Barandir; O. Unverdi; C. Celebi; L. O. Temur; D. C. Atilla; Deveci, D. Gemici; Celebi, C.; Atilla, D. C.; Temur, L. O.; Barandir, T. Karakoyun; Unverdi, O.
This study outlines the effectiveness of combining numerical methods Computer Vision (CV) and Machine Learning (ML) approaches to analyze and predict drift behavior in high-resolution Atomic Force Microscope (AFM) scanning procedures. Using Long Short-Term Memory (LSTM) models for time series analysis and the Light Gradient Boosting Machine (LightGBM) algorithm for predictive modeling significant progress was achieved in understanding the dynamic and variable nature of drift and mitigating its impact on scanning. The models demonstrated a robust predictive capability achieving approximately 94% accuracy in drift predictions. The study emphasizes the nonstationary characteristics of drift and demonstrates how the selection of features directly related to the target variable enhances the efficiency of the model and enables adaptive real-time correction. These findings confirm the predictive strength of the models and highlight the potential for integrating ML predictions with real-time feedback mechanisms to improve the resolution and stability of AFM imaging in both scientific and industrial applications.
Citation - WoS: 4
Citation - Scopus: 4
Light-induced modification of the Schottky barrier height in graphene/Si based near-infrared photodiodes
(ELSEVIER, 2022) M. Fidan; G. Donmez; A. Yanilmaz; O. Unverdi; C. Celebi; Donmez, G.; Fidan, M.; Celebi, C.; Yanilmaz, A.; Unverdi, O.
The impact of light on the Schottky barrier height (SBH) in p-type graphene/n-type Si (p-Gr/n-Si) based nearinfrared photodiodes is investigated. Hall effect and optoelectronic transport measurements carried out under illumination of 905 nm wavelength light showed that zero-bias SBH in such photodiodes can be effectively tuned in a range between 0.7 and 0.9 eV consistent with the variation in their open-circuit voltage. Shockley-Read-Hall model which considers the charge recombination through mid-gap and interface states at the p-Gr/n-Si heterojunction is used to explain the experimentally observed nonlinear dependence of SBH on the incident light. Light induced tunability of SBH at the graphene/semiconductor heterojunction is of great importance especially for the development of new generation optically driven devices in which graphene acts as a functioning element.