Learning-Based Data-Driven and Vision Methodology for Optimized Printed Electronics

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dc.contributor.advisorGrau, Gerd
dc.contributor.authorBrishty, Fahmida Pervin
dc.date.accessioned2022-12-14T16:16:55Z
dc.date.available2022-12-14T16:16:55Z
dc.date.copyright2020-11-04
dc.date.issued2022-12-14
dc.date.updated2022-12-14T16:16:54Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractInkjet printing is an active domain of additive manufacturing and printed electronics due to its promising features, starting from low-cost, scalability, non-contact printing, and microscale on-demand pattern customization. Up until now, mainstream research has been making headway in the development of ink material and printing process optimization through traditional methods, with almost no work concentrated on machine learning and vision-based drop behavior prediction, pattern generation, and enhancement. In this work, we first carry out a systematic piezoelectric drop on demand inkjet drop generation and characterization study to structure our dataset, which is later used to develop a drop formulation prediction module for diverse materials. Machine learning enables us to predict the drop speed and radius for particular material and printer electrical signal configuration. We verify our prediction results with untested graphene oxide ink. Thereafter, we study automated pattern generation and evaluation algorithms for inkjet printing via computer vision schema for several shapes, scales and finalize the best sequencing method in terms of comparative pattern quality, along with the underlying causes. In a nutshell, we develop and validate an automated vision methodology to optimize any given two-dimensional patterns. We show that traditional raster printing is inferior to other promising methods such as contour printing, segmented matrix printing, depending on the shape and dimension of the designed pattern. Our proposed vision-based printing algorithm eliminates manual printing configuration workload and is intelligent enough to decide on which segment of the pattern should be printed in which order and sequence. Besides, process defect monitoring and tracking has shown promising results equivalent to manual short circuit, open circuit, and sheet resistance testing for deciding over pattern acceptance or rejection with reduced device testing time. Drop behavior forecast, automatic pattern optimization, and defect quantization compared with the designed image allow dynamic adaptation of any materials properties with regards to any substrate and sophisticated design as established here with varying material properties; complex design features such as corners, edges, and miniature scale can be achieved.
dc.identifier.urihttp://hdl.handle.net/10315/40585
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer engineering
dc.subjectElectrical engineering
dc.subject.keywordsMachine learning
dc.subject.keywordsRandom forest
dc.subject.keywordsDecision tree
dc.subject.keywordsGradient boosting
dc.subject.keywordsEnsemble
dc.subject.keywordsVoting stacking
dc.subject.keywordsRMSE
dc.subject.keywordsJetting
dc.subject.keywordsContribution
dc.subject.keywordsBias
dc.subject.keywordsFeature engineering
dc.subject.keywordsImage processing
dc.subject.keywordsPrediction
dc.subject.keywordsClassification
dc.subject.keywordsInkjet printing
dc.subject.keywordsRegression
dc.subject.keywordsComputer vision
dc.subject.keywordsPrinted electronics
dc.subject.keywordsSymmetric printing
dc.subject.keywordsContour
dc.subject.keywordsRaster
dc.subject.keywordsVector
dc.subject.keywordsDefect
dc.subject.keywordsSegmentation
dc.subject.keywordsPrecision
dc.subject.keywordsRecall.
dc.titleLearning-Based Data-Driven and Vision Methodology for Optimized Printed Electronics
dc.typeElectronic Thesis or Dissertation

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