{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:03:21Z","timestamp":1780445001055,"version":"3.54.1"},"reference-count":45,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T00:00:00Z","timestamp":1658534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this paper, we study a new model that represents the symmetric connection between capacitance\u2013voltage and Schottky diode. This model has a symmetrical shape towards the horizontal direction. In recent times, works conducted on artificial neural network structure, which is one of the greatest actual artificial intelligence apparatuses used in various fields, stated that artificial neural networks are apparatuses that proposal very high forecast performance by the side of conventional structures. In the current investigation, an artificial neural network structure has been generated to guess the capacitance voltage productions of the Schottky diode with organic polymer edge, contingent on the frequency with a symmetrical shape. Of the dataset, 130 were grouped for training, 28 for validation, and 28 for testing. In order to evaluate the effect of the number of neurons on the prediction accuracy, three different models with different neuron numbers have been developed. This study, in which an artificial neural network model, although well-trained, could not predict the output values correctly, is a first in the literature. With this aspect, the study can be considered as a pioneering study that brings a novelty to the literature.<\/jats:p>","DOI":"10.3390\/sym14081511","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T04:52:47Z","timestamp":1658724767000},"page":"1511","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Do Artificial Neural Networks Always Provide High Prediction Performance? An Experimental Study on the Insufficiency of Artificial Neural Networks in Capacitance Prediction of the 6H-SiC\/MEH-PPV\/Al Diode"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9297-8134","authenticated-orcid":false,"given":"Anda\u00e7 Batur","family":"\u00c7olak","sequence":"first","affiliation":[{"name":"Mechanical Engineering Department, Ni\u011fde \u00d6mer Halisdemir University, Nigde 51240, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamer","family":"G\u00fczel","sequence":"additional","affiliation":[{"name":"Mecatronic Department, Ni\u011fde \u00d6mer Halisdemir University, Nigde 51200, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7186-7216","authenticated-orcid":false,"given":"Anum","family":"Shafiq","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7469-5402","authenticated-orcid":false,"given":"Kamsing","family":"Nonlaopon","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Khon Kaen University, Khon Kaen 40002, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","article-title":"A logical calculus of the ideas immanent in nervous activity","volume":"5","author":"McCulloch","year":"1943","journal-title":"Bull. Math. Biophys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s10462-011-9270-6","article-title":"Evolutionary artificial neural networks: A review","volume":"39","author":"Ding","year":"2013","journal-title":"Artif. Intell. Rev."},{"key":"ref_3","first-page":"184","article-title":"Application of artificial neural network in hydrology\u2014A review","volume":"4","author":"Tanty","year":"2015","journal-title":"Int. J. Eng. Technol. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Thakur, N., and Han, C.Y. (2021). Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments. Big Data Cogn. Comput., 5.","DOI":"10.3390\/bdcc5030042"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pavi\u0107evi\u0107, M., and Popovi\u0107, T. (2022). Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks. Sensors, 22.","DOI":"10.3390\/s22031051"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.rser.2013.08.055","article-title":"Solar radiation prediction using Artificial Neural Network techniques: A review","volume":"33","author":"Yadav","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.enbuild.2013.06.007","article-title":"Energy analysis of a building using artificial neural network: A review","volume":"65","author":"Kumar","year":"2013","journal-title":"Energy Build."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1016\/j.enconman.2010.06.031","article-title":"A review on electrochemical double-layer capacitors","volume":"51","author":"Sharma","year":"2010","journal-title":"Energy Convers. Manag."},{"key":"ref_9","unstructured":"Rhoderick, E., and Williams, R. (1988). Metal-Semiconductor Contacts, Clarendon."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/S0038-1101(98)00099-9","article-title":"A review of the metal\u2013GaN contact technology","volume":"42","author":"Liu","year":"1998","journal-title":"Solid-State Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1103\/PhysRevLett.73.2107","article-title":"Ferroelectric schottky diode","volume":"73","author":"Blom","year":"1994","journal-title":"Phys. Rev. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/0040-6090(78)90007-X","article-title":"A review of the theory, technology and applications of metal-semiconductor rectifiers","volume":"48","author":"Rideout","year":"1978","journal-title":"Thin Solid Film."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1142\/S0129156405003430","article-title":"Silicon carbide schottky barrier diode","volume":"15","author":"Zhao","year":"2005","journal-title":"Int. J. High Speed Electron. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8193","DOI":"10.1109\/TIE.2017.2652401","article-title":"Review of silicon carbide power devices and their applications","volume":"64","author":"She","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.24295\/CPSSTPEA.2020.00002","article-title":"Analysis of 600 V\/650 V SiC schottky diodes at extremely high temperatures","volume":"5","author":"Wang","year":"2020","journal-title":"CPSS Trans. Power Electron. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1109\/JEDS.2019.2913146","article-title":"Highly Reliable Inference System of Neural Networks Using Gated Schottky Diodes","volume":"7","author":"Lim","year":"2019","journal-title":"IEEE J. Electron. Devices Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106665","DOI":"10.1016\/j.spmi.2020.106665","article-title":"Optimal estimation of Schottky diode parameters using a novel optimization algorithm: Equilibrium optimizer","volume":"146","author":"Rabehi","year":"2020","journal-title":"Superlattices Microstruct."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.renene.2013.04.011","article-title":"Artificial neural network-based model for estimating the produced power of a photovoltaic module","volume":"60","author":"Mellit","year":"2013","journal-title":"Renew. Energy"},{"key":"ref_19","first-page":"47","article-title":"High Temperature Electronic Properties of a Microwave Frequency Sensor\u2013GaN Schottky Diode","volume":"15","author":"Alade","year":"2013","journal-title":"Adv. Phys. Theor. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.spmi.2015.03.033","article-title":"Optoelectronic performance and artificial neural networks (ANNs) modeling of n-InSe\/p-Si solar cell","volume":"83","author":"Darwish","year":"2015","journal-title":"Superlattices Microstruct."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.solener.2018.10.018","article-title":"Performance prediction of PV module using electrical equivalent model and artificial neural network","volume":"176","author":"Mittal","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_22","unstructured":"Liang, A., Xu, Y., Jia, S., and Sun, G. (2008, January 21\u201324). Neural networks for nonlinear modeling of microwave Schottky diodes. Proceedings of the International Conference on Microwave and Millimeter Wave Technology, Nanjing, China."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107062","DOI":"10.1016\/j.spmi.2021.107062","article-title":"Modeling of Schottky diode characteristic by machine learning techniques based on experimental data with wide temperature range","volume":"160","author":"Torun","year":"2021","journal-title":"Superlattices Microstruct."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1016\/j.ijheatmasstransfer.2018.04.035","article-title":"Prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger through artificial neural network technique","volume":"124","author":"Rahman","year":"2018","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.renene.2020.04.042","article-title":"Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons","volume":"156","author":"Pang","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ins.2018.07.049","article-title":"Partial multi-dividing ontology learning algorithm","volume":"467","author":"Gao","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.arabjc.2017.12.024","article-title":"Nano properties analysis via fourth multiplicative ABC indicator calculating","volume":"11","author":"Gao","year":"2018","journal-title":"Arab. J. Chem."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1016\/j.sjbs.2017.11.022","article-title":"Study of biological networks using graph theory","volume":"25","author":"Gao","year":"2018","journal-title":"Saudi J. Biol. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"877","DOI":"10.3934\/dcdss.2019058","article-title":"An independent set degree condition for fractional critical deleted graphs","volume":"12","author":"Gao","year":"2019","journal-title":"Discret. Contin. Dyn. Syst.-S"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"711","DOI":"10.3934\/dcdss.2019045","article-title":"Tight independent set neighborhood union condition for fractional critical deleted graphs and ID deleted graphs","volume":"12","author":"Dimitrov","year":"2019","journal-title":"Discret. Contin. Dyn. Syst.-S"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.eswa.2016.02.051","article-title":"Robust learning algorithm for multiplicative neuron model artificial neural networks","volume":"56","author":"Bas","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.icheatmasstransfer.2017.02.003","article-title":"A hybrid artificial neural network-genetic algorithm modeling approach for viscosity estimation of graphene nanoplatelets nanofluid using experimental data","volume":"82","author":"Vakili","year":"2017","journal-title":"Int. Commun. Heat Mass Transf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.powtec.2019.05.034","article-title":"Artificial intelligence in the field of nanofluids: A review on applications and potential future directions","volume":"353","author":"Bahiraei","year":"2019","journal-title":"Powder Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1162\/neco.1991.3.2.246","article-title":"Universal approximation using radial-basis-function networks","volume":"3","author":"Park","year":"1991","journal-title":"Neural Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.matcom.2020.04.031","article-title":"Application of the residue number system to reduce hardware costs of the convolutional neural network implementation","volume":"177","author":"Valueva","year":"2020","journal-title":"Math. Comput. Simul."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jcou.2017.03.011","article-title":"Experimental data, thermodynamic and neural network modeling of CO2 solubility in aqueous sodium salt of l-phenylalanine","volume":"19","author":"Garg","year":"2017","journal-title":"J. CO2 Util."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.molliq.2016.10.049","article-title":"An accurate RBF-NN model for estimation of viscosity of nanofluids","volume":"224","year":"2016","journal-title":"J. Mol. Liq."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.molliq.2017.02.015","article-title":"An inspection of thermal conductivity of CuO-SWCNTs hybrid nanofluid versus temperature and concentration using experimental data, ANN modeling and new correlation","volume":"231","author":"Rostamian","year":"2017","journal-title":"J. Mol. Liq."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"112307","DOI":"10.1016\/j.molliq.2019.112307","article-title":"The thermal conductivity, viscosity, and cloud points of bentonite nanofluids with n-pentadecane as the base fluid","volume":"300","author":"Esmaeilzadeh","year":"2020","journal-title":"J. Mol. Liq."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.flowmeasinst.2016.04.003","article-title":"Open channel junction velocity prediction by using a hybrid self-neuron adjustable artificial neural network","volume":"49","author":"Bonakdari","year":"2016","journal-title":"Flow Meas. Instrum."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.icheatmasstransfer.2016.03.008","article-title":"Prediction of thermal conductivity of various nanofluids using artificial neural network","volume":"74","author":"Ahmadloo","year":"2016","journal-title":"Int. Commun. Heat Mass Transf."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, J., Ayari, M.A., Khandakar, A., Chowdhury, M.E.H., Zaman, S.M.U., Rahman, T., and Vaferi, B. (2022). Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies. Polymers, 14.","DOI":"10.3390\/polym14030527"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.mee.2012.06.003","article-title":"Electrical and photoconductivity properties of p-Si\/P3HT\/Al and p-Si\/P3HT: MEH-PPV\/Al organic devices: Comparison study","volume":"98","author":"Gunduz","year":"2012","journal-title":"Microelectron. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.tsf.2014.01.036","article-title":"Electrical properties of Au\/polyvinylidene fluoride\/n-InP Schottky diode with polymer interlayer","volume":"556","author":"Reddy","year":"2014","journal-title":"Thin Solid Film."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1063\/1.336662","article-title":"Semiconductor analysis using organic-on-inorganic contact barriers. I. Theory of the effects of surface states on diode potential and ac admittance","volume":"59","author":"Forrest","year":"1986","journal-title":"J. Appl. Phys."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/8\/1511\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:34Z","timestamp":1760140534000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/8\/1511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,23]]},"references-count":45,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["sym14081511"],"URL":"https:\/\/doi.org\/10.3390\/sym14081511","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,23]]}}}