{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101705"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101705","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Analysis and simulation of carbon-based thermal interface material","abstract":"The student, Michael Jo, submitted this Dissertation for approval on 2018-07-11 at 12:35.","abstract_html":"The student, Michael Jo, submitted this Dissertation for approval on 2018-07-11 at 12:35.","abstract_has_math":false,"creators":["Jo, Michael Kim"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Ravaioli, Umberto","Lyding, Joseph W.","Aluru, Narayana R.","Schutt-Ainé, José E."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:34:18Z","date_published":"2018-09-27T16:34:18Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Dynamic Thermal Interface Material","Percolation Theory","Finite Difference","Machine Learning","Genetic Algorithm","Carbon Nanotube","Graphite"],"languages":["en"],"rights":["Copyright 2018 Michael Jo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101705","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ravaioli, Umberto","Lyding, Joseph W.","Aluru, Narayana R.","Schutt-Ainé, José E."]},{"key":"dc:creator","label":"Author","values":["Jo, Michael Kim"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:34:18Z","2020-09-28T09:15:07Z","2018-07-11","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Dynamic Thermal Interface Material","Percolation Theory","Finite Difference","Machine Learning","Genetic Algorithm","Carbon Nanotube","Graphite"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Michael Jo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101705"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Michael Jo, submitted this Dissertation for approval on 2018-07-11 at 12:35.","This Dissertation was approved for publication on 2018-07-11 at 16:16.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12825 on 2018-09-27 at 11:19:05","Made available in DSpace on 2018-09-27T16:34:18Z (GMT). No. of bitstreams: 2 JO-DISSERTATION-2018.pdf: 14424230 bytes, checksum: 59ba4f16908a02403f1dc647362e8eaf (MD5) LICENSE.txt: 4207 bytes, checksum: 53eea5e1aa09321be40aaa993b2cec4c (MD5) Previous issue date: 2018-07-11","Dynamic-Thermal Interface Material (D-TIM) is an adaptive thermal interface material with thermal conductivity proportionally increasing with system temperature. This thesis introduces a percolation theory-based simulation to investigate this unique trend. Brief reviews of the important aspects of carbon-based thermal interface material and the concept of percolation theory are discussed. In order to capture the thermal conductivity enhancement with temperature, we employ thermoelectric effect and variable-range hopping as the governing equations of the simulation. The complete framework of D-TIM percolation simulation and simulation results are presented. On the other hand, machine learning has been successfully adopted by the industry since current computation techniques can manage massive calculations for such algorithms. However, most of these successes are based on software, computer architecture and circuit design level. On the contrary, machine learning for analysis of data in micro-nanotechnology has not been widely applied, resulting in ample space for researchers to extract inference not only from experimental data but also from simulation data. Here, we present examples of machine learning algorithms applied to experiment data. After a brief review of machine learning algorithms, we present three complete projects as examples. The first example demonstrates a technique to extract geometric properties from Raman spectra of few-layer graphenes. Secondly, we present how to detect Chevron Graphene Nanoribbon (CGNR) from scanning tunneling spectroscopy data and calculate the orientation autonomously. Lastly, the third project is to remove silicon substrate effect from the measured current imaging tunneling spectroscopy in order to extract the pure local density of states of the CGNR. Then, we use machine learning, genetic algorithm in this problem, for parameter optimization of D-TIM simulation. We discuss the optimized parameters of D-TIM percolation simulation and in turn analyze the mass ratio of nano-graphite and multi-wall carbon nanotube to improve the effective thermal conductivity of D-TIM. Finally, we analyze the mass ratio and surface roughness effect on D-TIM performance.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Michael Jo, accepted the attached license on 2018-07-11 at 12:25.","Embargo set by: Seth Robbins for item 107805 Lift date: 2020-09-27T16:34:29Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 107805 on 2020-09-28T09:15:07Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Analysis and simulation of carbon-based thermal interface material"]}]}],"canonical_facts":{"dc:contributor":["Ravaioli, Umberto","Lyding, Joseph W.","Aluru, Narayana R.","Schutt-Ainé, José E."],"dc:creator":["Jo, Michael Kim"],"dc:date":["2018-09-27T16:34:18Z","2020-09-28T09:15:07Z","2018-07-11","2018-08"],"dc:description":["The student, Michael Jo, submitted this Dissertation for approval on 2018-07-11 at 12:35.","This Dissertation was approved for publication on 2018-07-11 at 16:16.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12825 on 2018-09-27 at 11:19:05","Made available in DSpace on 2018-09-27T16:34:18Z (GMT). No. of bitstreams: 2 JO-DISSERTATION-2018.pdf: 14424230 bytes, checksum: 59ba4f16908a02403f1dc647362e8eaf (MD5) LICENSE.txt: 4207 bytes, checksum: 53eea5e1aa09321be40aaa993b2cec4c (MD5) Previous issue date: 2018-07-11","Dynamic-Thermal Interface Material (D-TIM) is an adaptive thermal interface material with thermal conductivity proportionally increasing with system temperature. This thesis introduces a percolation theory-based simulation to investigate this unique trend. Brief reviews of the important aspects of carbon-based thermal interface material and the concept of percolation theory are discussed. In order to capture the thermal conductivity enhancement with temperature, we employ thermoelectric effect and variable-range hopping as the governing equations of the simulation. The complete framework of D-TIM percolation simulation and simulation results are presented. On the other hand, machine learning has been successfully adopted by the industry since current computation techniques can manage massive calculations for such algorithms. However, most of these successes are based on software, computer architecture and circuit design level. On the contrary, machine learning for analysis of data in micro-nanotechnology has not been widely applied, resulting in ample space for researchers to extract inference not only from experimental data but also from simulation data. Here, we present examples of machine learning algorithms applied to experiment data. After a brief review of machine learning algorithms, we present three complete projects as examples. The first example demonstrates a technique to extract geometric properties from Raman spectra of few-layer graphenes. Secondly, we present how to detect Chevron Graphene Nanoribbon (CGNR) from scanning tunneling spectroscopy data and calculate the orientation autonomously. Lastly, the third project is to remove silicon substrate effect from the measured current imaging tunneling spectroscopy in order to extract the pure local density of states of the CGNR. Then, we use machine learning, genetic algorithm in this problem, for parameter optimization of D-TIM simulation. We discuss the optimized parameters of D-TIM percolation simulation and in turn analyze the mass ratio of nano-graphite and multi-wall carbon nanotube to improve the effective thermal conductivity of D-TIM. Finally, we analyze the mass ratio and surface roughness effect on D-TIM performance.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Michael Jo, accepted the attached license on 2018-07-11 at 12:25.","Embargo set by: Seth Robbins for item 107805 Lift date: 2020-09-27T16:34:29Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 107805 on 2020-09-28T09:15:07Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/101705"],"dc:language":["en"],"dc:rights":["Copyright 2018 Michael Jo"],"dc:subject":["Dynamic Thermal Interface Material","Percolation Theory","Finite Difference","Machine Learning","Genetic Algorithm","Carbon Nanotube","Graphite"],"dc:title":["Analysis and simulation of carbon-based thermal interface material"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:40Z"}