Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386214657> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W4386214657 abstract "Making repeatable devices is a significant obstacle to the widespread adoption of printed electronics. Due to the complexity and sensitivity of inkjet or extrusion printing physics, it is difficult to use simple models to make predictions that match measured devices. To overcome this challenge, we developed an AI-based approach to predict experimental values. We employed two different printers to create a test pattern with various controllable parameters, and an automated prober was developed to measure wire resistances across a large printed area. Image processing was used on scanned PCB images to extract texture data from the printed structures. A machine learning model was developed to predict the test structures' resistance from only their geometric parameters and texture data. In our best single-board experiments, the root mean square error between the predicted and measured values was within <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$0.01 Omega$</tex> . This model can be used to design structures that achieve desired resistances by actively controlling the print texture." @default.
- W4386214657 created "2023-08-29" @default.
- W4386214657 creator A5011835422 @default.
- W4386214657 creator A5013721686 @default.
- W4386214657 creator A5022416957 @default.
- W4386214657 creator A5027943095 @default.
- W4386214657 creator A5047533805 @default.
- W4386214657 creator A5050048804 @default.
- W4386214657 creator A5067370289 @default.
- W4386214657 creator A5092706466 @default.
- W4386214657 date "2023-07-09" @default.
- W4386214657 modified "2023-09-30" @default.
- W4386214657 title "Feature-Based Machine Learning for Predicting Resistances in Printed Electronics" @default.
- W4386214657 cites W1965229818 @default.
- W4386214657 cites W2058333183 @default.
- W4386214657 cites W2095905764 @default.
- W4386214657 cites W2334297625 @default.
- W4386214657 cites W2767798807 @default.
- W4386214657 cites W2771813345 @default.
- W4386214657 doi "https://doi.org/10.1109/fleps57599.2023.10220406" @default.
- W4386214657 hasPublicationYear "2023" @default.
- W4386214657 type Work @default.
- W4386214657 citedByCount "0" @default.
- W4386214657 crossrefType "proceedings-article" @default.
- W4386214657 hasAuthorship W4386214657A5011835422 @default.
- W4386214657 hasAuthorship W4386214657A5013721686 @default.
- W4386214657 hasAuthorship W4386214657A5022416957 @default.
- W4386214657 hasAuthorship W4386214657A5027943095 @default.
- W4386214657 hasAuthorship W4386214657A5047533805 @default.
- W4386214657 hasAuthorship W4386214657A5050048804 @default.
- W4386214657 hasAuthorship W4386214657A5067370289 @default.
- W4386214657 hasAuthorship W4386214657A5092706466 @default.
- W4386214657 hasConcept C111919701 @default.
- W4386214657 hasConcept C115961682 @default.
- W4386214657 hasConcept C119599485 @default.
- W4386214657 hasConcept C119857082 @default.
- W4386214657 hasConcept C120793396 @default.
- W4386214657 hasConcept C127413603 @default.
- W4386214657 hasConcept C138331895 @default.
- W4386214657 hasConcept C138885662 @default.
- W4386214657 hasConcept C154945302 @default.
- W4386214657 hasConcept C199639397 @default.
- W4386214657 hasConcept C21200559 @default.
- W4386214657 hasConcept C24326235 @default.
- W4386214657 hasConcept C2776401178 @default.
- W4386214657 hasConcept C2781195486 @default.
- W4386214657 hasConcept C41008148 @default.
- W4386214657 hasConcept C41895202 @default.
- W4386214657 hasConceptScore W4386214657C111919701 @default.
- W4386214657 hasConceptScore W4386214657C115961682 @default.
- W4386214657 hasConceptScore W4386214657C119599485 @default.
- W4386214657 hasConceptScore W4386214657C119857082 @default.
- W4386214657 hasConceptScore W4386214657C120793396 @default.
- W4386214657 hasConceptScore W4386214657C127413603 @default.
- W4386214657 hasConceptScore W4386214657C138331895 @default.
- W4386214657 hasConceptScore W4386214657C138885662 @default.
- W4386214657 hasConceptScore W4386214657C154945302 @default.
- W4386214657 hasConceptScore W4386214657C199639397 @default.
- W4386214657 hasConceptScore W4386214657C21200559 @default.
- W4386214657 hasConceptScore W4386214657C24326235 @default.
- W4386214657 hasConceptScore W4386214657C2776401178 @default.
- W4386214657 hasConceptScore W4386214657C2781195486 @default.
- W4386214657 hasConceptScore W4386214657C41008148 @default.
- W4386214657 hasConceptScore W4386214657C41895202 @default.
- W4386214657 hasFunder F4320338295 @default.
- W4386214657 hasLocation W43862146571 @default.
- W4386214657 hasOpenAccess W4386214657 @default.
- W4386214657 hasPrimaryLocation W43862146571 @default.
- W4386214657 hasRelatedWork W2961085424 @default.
- W4386214657 hasRelatedWork W3046775127 @default.
- W4386214657 hasRelatedWork W3170094116 @default.
- W4386214657 hasRelatedWork W3209574120 @default.
- W4386214657 hasRelatedWork W4205958290 @default.
- W4386214657 hasRelatedWork W4285260836 @default.
- W4386214657 hasRelatedWork W4286629047 @default.
- W4386214657 hasRelatedWork W4306321456 @default.
- W4386214657 hasRelatedWork W4306674287 @default.
- W4386214657 hasRelatedWork W4224009465 @default.
- W4386214657 isParatext "false" @default.
- W4386214657 isRetracted "false" @default.
- W4386214657 workType "article" @default.