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dc.contributor.advisorDiebold, Alaine (Committee member)
dc.contributor.advisorVentrice, Carl A. Jr. (Committee member)
dc.contributor.advisorLloyd, James (Committee member)
dc.contributor.advisorKurinec, Santosh (External committee member)
dc.contributor.advisorCady, Nathaniel (Dissertation Committee Chair)
dc.contributor.authorHazra, Jubin
dc.date.accessioned2021-08-31T21:26:23Z
dc.date.available2021-08-31T21:26:23Z
dc.date.issued2021-08
dc.identifier.urihttp://hdl.handle.net/20.500.12648/2061
dc.description.abstractFundamental limitations associated with scaling and modern von Neumann computing architectures illustrates the need for emerging memory solutions in the semiconductor industry. One such promising non-volatile memory (NVM) solution is resistive random access memory (RRAM), which is seen as a potential candidate that can meet the performance needs of DRAM and the density of NAND Flash in terms of scalability, reliability and switching performance. However, reliable operation of RRAM devices requires further development to remedy device- to-device and cycle-to-cycle uniformity variation, increase the conductance window, and to improve retention, yield and endurance properties. This research work primarily focuses on improving RRAM performance metrics through optimization of processing conditions and programming algorithms for CMOS-integrated nanoscale HfO2 RRAM devices on a full scale 300mm wafer platform. It was observed that tuning of ALD parameters during RRAM switching layer HfO2 deposition had a significant impact on device switching performance. An excellent memory window of >30 with switching yield ~90%, along with low cycle-to-cycle (σ <0.5) and cell-to-cell variability (σ <0.4) were achieved for tested 1 Transistor 1 RRAM (1T1R) cells across full 300mm wafers. The devices demonstrated excellent endurance (>1010 switching cycles) and data retention performance at elevated temperature (105 s at 373K). The fabricated RRAM cells were also optimized for multi-level-cell switching behavior and ~10 distinct resistance levels were obtained through a combined current- and voltage-control based programming approach. An incremental pulse write technique combined with read verification algorithm enabled accurate resistance states programming within a large resistance window along with linear and symmetric potentiation-depression characteristics yielding superior analog synaptic functionality of fabricated RRAM devices. In addition to RRAM devices, hafnium zirconium oxide (HZO) based nanoscale ferroelectric tunnel junction (FTJ) devices were successfully implemented on a 300 mm wafer platform. Current measurement, as a function of voltage for both up and down polarization states, yielded a tunneling electroresistance (TER) ratio of ~5 and switching endurance up to 106 cycles in TiN/ Al2O3/ Hf0.5Zr0.5O2/ TiN FTJ devices distributed across full 300 mm wafer. Investigation of current transport mechanisms showed that the conduction in these FTJ devices is dominated by direct tunneling (DT) at low electric field and by Fowler-Nordheim (F-N) tunneling at high electric field. The realization of CMOS-compatible nanoscale RRAM and FTJ devices on 300mm wafers demonstrates the promising potential of these devices in large scale high-yield NVM manufacturing for high performance embedded memory and mass data storage applications.en_US
dc.language.isoen_USen_US
dc.subjectnon-volatile memory (NVM)en_US
dc.subjectresistive random-access memory (RRAM)en_US
dc.subjectvon Neumann computing architecturesen_US
dc.subjectDynamic random-access memory (DRAM)en_US
dc.subjectNAND Flashen_US
dc.subjecthigh performance embedded memoryen_US
dc.subjectmass data storageen_US
dc.titleDevelopment of High-Performance Hafnium Oxide based Non-Volatile Memory Devices on 300mm Wafer Platform for Data Storage and Neuromorphic Applicationsen_US
dc.typeDissertationen_US
dc.description.versionNAen_US
refterms.dateFOA2021-08-31T21:26:23Z
dc.description.institutionSUNY Polytechnic Instituteen_US
dc.description.departmentDepartment of Nanoscale Science & Engineeringen_US
dc.description.degreelevelPhDen_US


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