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Dergiler / Artificial Intelligence Theory and Applications / 2026 / Volume: 3 Issue: 1

Effects of PCPDTBT:PCBM Ratio on the Electrical Analysis and the Prediction Of I-V Data Using Machine Learning Algorithms for Au/PCPDTBT:PCBM/n-Si MPS SBDs

Ömer Berkan Çelik(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0009-0004-0116-766X
Burak Taş(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0000-0002-9928-5004
Özgün Uz(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0000-0002-6752-2861
Hüseyin Muzaffer Şağban(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0000-0001-8820-5622
Özge Tüzün Özmen(İZMİR BAKIRÇAY ÜNİVERSİTESİ)0000-0002-5204-3737
Sayfa
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Yayın Tarihi
2023-05-01
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The obtained results indicate that certain algorithms can predict the I-V data of Au/PCPDTBT:PCBM/n-Si MPS SBD with full accuracy, i.e., 100%.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In this study, Au/Poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b′]dithiophene)-alt-4,7(2,1,3-benzothiadiazole)] (PCPDTBT) : [6,6]-phenyl C61 butyric acid methyl ester (PCBM) /n-Si heterojunction Schottky barrier diodes (SBDs) with 1:1 and 2:1 PCPDTBT:PCBM doping ratios were produced, and the electrical analysis of metal-polimer-semiconductor (MPS) SBDs with different concentrations was investigated.
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Temel Bulgular & Veri:Ideality factor (n), saturation current values (I0) and barrier heights (F0) of the materials were obtained based on the current-voltage (I-V) measurements performed.
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Sonuç & Katkı:The obtained results indicate that certain algorithms can predict the I-V data of Au/PCPDTBT:PCBM/n-Si MPS SBD with full accuracy, i.e., 100%.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2 samples
⚙️Değişkenler:Schottky barrier diode · PCPDTBT:PCBM ratio · electrical analysis

Abstract

In this study, Au/Poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b′]dithiophene)-alt-4,7(2,1,3-benzothiadiazole)] (PCPDTBT) : [6,6]-phenyl C61 butyric acid methyl ester (PCBM) /n-Si heterojunction Schottky barrier diodes (SBDs) with 1:1 and 2:1 PCPDTBT:PCBM doping ratios were produced, and the electrical analysis of metal-polimer-semiconductor (MPS) SBDs with different concentrations was investigated. Ideality factor (n), saturation current values (I0) and barrier heights (F0) of the materials were obtained based on the current-voltage (I-V) measurements performed. According to the results obtained, the PCBM concentration has significant effects on the electrical properties of the Au/PCPDTBT:PCBM/n-Si MPS SBD. To predict the electrical characterization of a system in detail, based on its doping concentration, the I-V data set consisting of 2 samples is typically split into a 70% training set and a 30% test set, which is used to train machine learning algorithms. Various methods, including Fine Tree, Cubic SVM, Fine KNN, Boosted Trees, Bagged Trees, Subspace KNN, RUSBoosted Trees, Wide Neural Network, Trilayered Neural Network, and Logistic Regression Kernel, have been analyzed. The obtained results indicate that certain algorithms can predict the I-V data of Au/PCPDTBT:PCBM/n-Si MPS SBD with full accuracy, i.e., 100%.

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Bu Makaleye Atıf Yap

Çelik, Ö. B., Taş, B., Uz, Ö., Şağban, H. M., Özmen, Ö. T. (2023). Effects of PCPDTBT:PCBM Ratio on the Electrical Analysis and the Prediction Of I-V Data Using Machine Learning Algorithms for Au/PCPDTBT:PCBM/n-Si MPS SBDs. Artificial Intelligence Theory and Applications, Volume: 3 Issue: 1. https://ajindex.com/articles/1609621

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