prikaz prve stranice dokumenta Modeliranje memristora
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undergraduate thesis
Modeliranje memristora
2023. urn:nbn:hr:168:387995
Kukić, Patrik
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Kukić, P. (2023). Modeliranje memristora (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:387995

Kukić, Patrik. "Modeliranje memristora." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:387995

Kukić, Patrik. "Modeliranje memristora." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:387995

Kukić, P. (2023). 'Modeliranje memristora', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 10 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:387995

Kukić P. Modeliranje memristora [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 November 10] Available at: https://urn.nsk.hr/urn:nbn:hr:168:387995

P. Kukić, "Modeliranje memristora", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:387995

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Metadata
TitleModeliranje memristora
Title (english)Memristor modelling
AuthorPatrik Kukić
MentorDario Bojanjac (mentor)
Committee memberDario Bojanjac (predsjednik povjerenstva)
Committee memberDominik Žanić (član povjerenstva)
Committee memberFilip Turčinović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-06-29, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U radu je prikazana nova metoda određivanja optimalnog oblika gornje elektrode u RRAM ćelijama s promjenom valencije. Konstruiran je stohastički model rasta vodljivog vlakna (CF), koji se fokusirao samo na fazu formiranja. Napon prekida simulacije korišten je kao procjena za određivanje varijabilnosti vodljivosti u stanju niskog otpora. Optimizacija oblika gornje elektrode provedena je pomoću genetskog algoritma. Primijenjena je inovativna strategija mutacije, koja uzima u obzir povijest populacije, uz strategiju odabira putem eksponencijalne rang selekcije. Optimalni oblik elektrode, do broja epoha genetskog algoritma, prikazan je u odjeljku Rezultati.
Abstract (english)
The paper presents a novel method of determining optimal top electrode shape in valence change RRAM cells. A model of stochastic conductive filament (CF) growth was constructed, which focused only on the formation phase. The cutoff voltage was used as a proxy for determining the conductance variability in the low resistance state. Optimization of the top electrode shape was performed using a genetic algorithm. An innovative mutation strategy, which takes into account the history of the population, was employed, alongside an exponential rank selection strategy. The optimal electrode shape, up to the number of genetic algorithm epochs, is presented in the Results section.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:387995
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-07-13 10:14:07