{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T20:02:41Z","timestamp":1770148961881,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SeoulTech (Seoul National University of Science and Technology)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>This study investigates supervised learning to improve LED classification. A hardware system for testing was built. The data for learning were acquired and then analyzed to show their characteristics. An LED was tested, and the results were categorized into three defective LED groups and one normal LED group. Before classification, electrical and optical data were examined to identify their characteristics. To find out the best way for quality control, an ensemble of methods was used. First, the discriminant analysis using the validation data achieved a 77.9% true positive rate for normal products, inadequate for quality control. Second, neural network-based learning boosted this rate to 97.8%, but the 2.2% false negative rate remained problematic. Finally, a binary decision tree was constructed, achieving a 99.4% true positive rate from just 14 splits, proving highly effective in product classification. The training time was measured as 8.1, 18.2 and 8.2 s for discriminant analysis, neural network and decision tree, respectively. This work has found the binary decision tree is advantageous considering both learning and classification efficiencies.<\/jats:p>","DOI":"10.3390\/mi15040457","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T12:52:12Z","timestamp":1711630332000},"page":"457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Classification of LED Packages for Quality Control by Discriminant Analysis, Neural Network and Decision Tree"],"prefix":"10.3390","volume":"15","author":[{"given":"Heesoo","family":"Shim","sequence":"first","affiliation":[{"name":"Department of Mechanical System Design Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6799-4247","authenticated-orcid":false,"given":"Sun Kyoung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical System Design Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/JDT.2007.895339","article-title":"Status and future of high-power light-emitting diodes for solid-state lighting","volume":"3","author":"Krames","year":"2007","journal-title":"J. Display Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100263","DOI":"10.1016\/j.pquantelec.2020.100263","article-title":"Growth, transfer printing and colour conversion techniques towards full-colour micro-LED display","volume":"71","author":"Zhou","year":"2020","journal-title":"Prog. Quantum Electron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1038\/nphoton.2007.34","article-title":"The life and times of the LED\u2014A 100-year history","volume":"1","author":"Zheludev","year":"2007","journal-title":"Nature Photon."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wu, Y., Ma, J., Su, P., Zhang, L., and Xia, B. (2020). Full-color realization of micro-LED displays. Nanomaterials, 10.","DOI":"10.3390\/nano10122482"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"041315","DOI":"10.1063\/1.5096322","article-title":"Beyond solid-state lighting: Miniaturization, hybrid integration, and applications of GaN nano-and micro-LEDs","volume":"6","author":"Wasisto","year":"2019","journal-title":"Appl. Phys. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Held, G. (2016). Introduction to Light Emitting Diode Technology and Applications, CRC Press.","DOI":"10.1201\/9781420076639"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Masui, H. (2023). Introduction to the Light-Emitting Diode: Real Applications for Industrial Engineers, Springer Nature.","DOI":"10.1007\/978-3-031-30716-4"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1007\/s00170-011-3338-y","article-title":"Automated SMD LED inspection using machine vision","volume":"57","author":"Perng","year":"2011","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1007\/s10845-012-0725-7","article-title":"Automatic inspection system of LED chip using two-stages back-propagation neural network","volume":"25","author":"Kuo","year":"2014","journal-title":"J. Intell. Manuf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1007\/s10845-018-1415-x","article-title":"Automated defect inspection of LED chip using deep convolutional neural network","volume":"30","author":"Lin","year":"2019","journal-title":"J. Intell. Manuf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sheng, H., and Nisar, M.S. (2024). Simulating an Integrated Photonic Image Classifier for Diffractive Neural Networks. Micromachines, 15.","DOI":"10.3390\/mi15010050"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3882","DOI":"10.1016\/j.rinp.2017.09.036","article-title":"An investigation on die crack detection using Temperature Sensitive Parameter for high speed LED mass production","volume":"7","author":"Devarajan","year":"2017","journal-title":"Results Phys."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jmsy.2023.09.006","article-title":"Top ten intelligent algorithms towards smart manufacturing","volume":"71","author":"Zhang","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TED.2023.3307051","article-title":"Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques","volume":"71","author":"Wang","year":"2024","journal-title":"IEEE Trans. Electron Devices"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5448","DOI":"10.1109\/TED.2020.3032634","article-title":"Machine Learning-Based Statistical Approach to Analyze Process Dependencies on Threshold Voltage in Recessed Gate AlGaN\/GaN MIS-HEMTs","volume":"67","author":"Wu","year":"2020","journal-title":"IEEE Trans. Electron Devices"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/TED.2023.3251296","article-title":"Machine Learning Augmented Compact Modeling for Simultaneous Improvement in Computational Speed and Accuracy","volume":"71","author":"Sheelvardhan","year":"2024","journal-title":"IEEE Trans. Electron Devices"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ba, F., Peng, P., Zhang, Y., and Zhao, Y. (2023). Classification and Identification of Contaminants in Recyclable Containers Based on a Recursive Feature Elimination-Light Gradient Boosting Machine Algorithm Using an Electronic Nose. Micromachines, 14.","DOI":"10.3390\/mi14112047"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.jmsy.2022.04.016","article-title":"Toward smart manufacturing: Analysis and classification of cutting parameters and energy consumption patterns in turning processes","volume":"64","author":"Ragai","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.jmsy.2021.10.010","article-title":"Quality detection and classification for ultrasonic welding of carbon fiber composites using time-series data and neural network methods","volume":"61","author":"Sun","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101005","DOI":"10.1115\/1.4062457","article-title":"Machine Learning Tools for Flow-Related Defects Detection in Friction Stir Welding","volume":"145","author":"Ambrosio","year":"2023","journal-title":"ASME J. Manuf. Sci. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3075","DOI":"10.1080\/0020754021000049817","article-title":"Classification scheme for lean manufacturing tools","volume":"41","author":"Pavnaskar","year":"2003","journal-title":"Int. J. Prod. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.jmsy.2021.09.009","article-title":"Deep ensemble learning with non-equivalent costs of fault severities for rolling bearing diagnostics","volume":"61","author":"Yang","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Essid, O., Laga, H., and Samir, C. (2018). Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0203192"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"091005","DOI":"10.1115\/1.4036641","article-title":"Classifying the Dimensional Variation in Additive Manufactured Parts from Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches","volume":"139","author":"Dsouza","year":"2017","journal-title":"ASME J. Manuf. Sci. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jmsy.2023.03.001","article-title":"Classification of primitive manufacturing tasks from filtered event data","volume":"68","author":"Duarte","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shi, X., Sun, Y., Tian, H., Abhilash, P.M., Luo, X., and Liu, H. (2023). Material Extrusion Filament Width and Height Prediction via Design of Experiment and Machine Learning. Micromachines, 14.","DOI":"10.3390\/mi14112091"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1109\/LRA.2017.2714135","article-title":"Categorization of anomalies in smart manufacturing systems to support the selection of detection mechanisms","volume":"2","author":"Lopez","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/JAS.2020.1003114","article-title":"AI-based modeling and data-driven evaluation for smart manufacturing processes","volume":"7","author":"Ghahramani","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_29","unstructured":"(2024, March 17). Guidelines for Principal Component Reliability Testing for LED Light Sources and LED Luminaires, IEC TS 62861. Available online: https:\/\/webstore.iec.ch\/publication\/30563."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1016\/j.measurement.2011.04.003","article-title":"Graphic supervisory control of an automatic optical inspection for LED properties","volume":"44","author":"Fung","year":"2011","journal-title":"Measurement"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1002\/sdtp.15949","article-title":"High Resolution and High Speed Inspection Equipment for Mini-LED Substrates","volume":"Volume 53","author":"Yasuda","year":"2022","journal-title":"SID Symposium Digest of Technical Papers"},{"key":"ref_32","unstructured":"(2017). Amendment 6\u2014Double-Capped Fluorescent Lamps\u2014Performance Specifications (Standard No. IEC 60081:1997\/AMD6:2017)."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"093104","DOI":"10.1063\/1.3009966","article-title":"High power light-emitting diode junction temperature determination from current-voltage characteristics","volume":"104","author":"Keppens","year":"2008","journal-title":"J. Appl. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1002\/lpor.201200025","article-title":"Efficiency droop in light-emitting diodes: Challenges and countermeasures","volume":"7","author":"Cho","year":"2013","journal-title":"Laser Photonics Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1016\/j.jcrysgro.2008.09.123","article-title":"Damage of light-emitting diodes induced by high reverse-bias stress","volume":"311","author":"Chen","year":"2009","journal-title":"J. Cryst. Growth"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8358","DOI":"10.1364\/OE.418843","article-title":"Monolithic integration of ultraviolet light emitting diodes and photodetectors on a p-GaN\/AlGaN\/GaN\/Si platform","volume":"29","author":"Lyu","year":"2021","journal-title":"Opt. Express"},{"key":"ref_37","first-page":"67","article-title":"Novel approach for LED luminous intensity measurement","volume":"6910","author":"Rykowski","year":"2008","journal-title":"Light-Emit. Diodes Res. Manuf. Appl. XII"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"11712","DOI":"10.1109\/ACCESS.2017.2716781","article-title":"Improving peak-wavelength method to measure junction temperature by dual-wavelength LEDs","volume":"5","author":"Zhao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_39","first-page":"51","article-title":"Quantitative Analysis of Color Combination from LED and Laser Light Sources Using Modified CIE 1931 Color Space Coordinates","volume":"49","author":"Sugiarto","year":"2017","journal-title":"J. Math. Fundam. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jmsy.2018.04.001","article-title":"Porosity prediction: Supervised-learning of thermal history for direct laser deposition","volume":"47","author":"Khanzadeh","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TSM.2015.2497264","article-title":"Wafer Map Defect Detection and Recognition Using Joint Local and Nonlocal Linear Discriminant Analysis","volume":"29","author":"Yu","year":"2016","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.1016\/j.compscitech.2004.02.012","article-title":"Characterization of local preform defects in resin transfer molding by the gas flow method and statistical analysis","volume":"64","author":"Opperer","year":"2004","journal-title":"Compos. Sci. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/01621459.1989.10478752","article-title":"Regularized Discriminant Analysis","volume":"84","author":"Friedman","year":"1989","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chemolab.2008.07.010","article-title":"Comparison of performance of five common classifiers represented as boundary methods","volume":"95","author":"Dixon","year":"2009","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_45","unstructured":"Agarap, A.F. (2018). Deep learning using rectified linear units (relu). arXiv."},{"key":"ref_46","unstructured":"Memisevic, R., Zach, C., Pollefeys, M., and Hinton, G.E. (2010). Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e0134254","DOI":"10.1371\/journal.pone.0134254","article-title":"Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the \u2018extreme learning machine\u2019algorithm","volume":"10","author":"McDonnell","year":"2015","journal-title":"PLoS ONE"},{"key":"ref_48","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Van der Aalst, W. (2016). Process Mining, Springer. [2nd ed.].","DOI":"10.1007\/978-3-662-49851-4"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1080\/0951192X.2017.1407447","article-title":"Imbalanced classification of manufacturing quality conditions using cost-sensitive decision tree ensembles","volume":"31","author":"Kim","year":"2018","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1016\/j.promfg.2017.07.326","article-title":"Decision Trees for Supervised Multi-criteria Inventory Classification","volume":"11","author":"Lolli","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6412","DOI":"10.1016\/j.eswa.2013.05.047","article-title":"A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information","volume":"40","author":"Evans","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.jmsy.2018.07.003","article-title":"A tree-based decision method for the configuration design of reconfigurable machine tools","volume":"49","author":"Wang","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.cirpj.2017.12.003","article-title":"Decision trees for implementing rapid manufacturing for mass customization","volume":"23","author":"Deradjat","year":"2018","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_55","unstructured":"Bramer, M. (2007). Principles of Data Mining, Springer."},{"key":"ref_56","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Chapman & Hall."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1002\/widm.8","article-title":"Classification and regression trees","volume":"1","author":"Loh","year":"2011","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jmsy.2023.05.027","article-title":"Multilayer Grad-CAM: An effective tool towards explainable deep neural networks for intelligent fault diagnosis","volume":"69","author":"Li","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.jmsy.2023.03.010","article-title":"Classification of handover interaction primitives in a COBOT\u2013human context with a deep neural network","volume":"68","author":"Castro","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"102144","DOI":"10.1016\/j.ndteint.2019.102144","article-title":"Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning","volume":"107","author":"Du","year":"2019","journal-title":"Ndt E Int."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.jmsy.2020.03.009","article-title":"Automated defect inspection system for metal surfaces based on deep learning and data augmentation","volume":"55","author":"Yun","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TPAMI.1979.4766926","article-title":"A problem of dimensionality: A simple example","volume":"3","author":"Trunk","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.neunet.2023.08.017","article-title":"Bidirectionally self-normalizing neural networks","volume":"167","author":"Lu","year":"2023","journal-title":"Neural Netw."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Kelleher, J.D. (2019). Deep Learning, MIT Press.","DOI":"10.7551\/mitpress\/11171.001.0001"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"071018","DOI":"10.1115\/1.4036350","article-title":"A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests","volume":"139","author":"Wu","year":"2017","journal-title":"ASME J. Manuf. Sci. Eng."}],"container-title":["Micromachines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-666X\/15\/4\/457\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:20:22Z","timestamp":1760106022000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-666X\/15\/4\/457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,28]]},"references-count":65,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["mi15040457"],"URL":"https:\/\/doi.org\/10.3390\/mi15040457","relation":{},"ISSN":["2072-666X"],"issn-type":[{"value":"2072-666X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,28]]}}}