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Indiana University-Purdue University Indianapolis (IUPUI) / With the rise of big data and the demands for leveraging artificial intelligence (AI), healthcare requires more knowledge sharing that offers machine-readable semantic formalization. Even though some applications allow shared data interoperability, they still lack formal machine-readable semantics in ICD9/10 and LOINC. With ontology, the further ability to represent the shared conceptualizations is possible, similar to SNOMED-CT. Nevertheless, SNOMED-CT mainly focuses on electronic health record (EHR) documenting and evidence-based practice. Moreover, due to its independence on data quality, the ontology enhances advanced AI technologies, such as machine learning (ML), by providing a reusable knowledge framework. Developing a machine-readable and sharable semantic knowledge model incorporating external evidence and individual practice’s values will create a new revolution for best practice medicine. The purpose of this research is to implement a sharable ontology for the best practice in healthcare, with anterior cruciate ligament reconstruction (ACLR) as a case study. The ontology represents knowledge derived from both evidence-based practice (EBP) and practice-based evidence (PBE). First, the study presents how the domain-specific knowledge model is built using a combination of Toronto Virtual Enterprise (TOVE) and a bottom-up approach. Then, I propose a top-down approach using Open Biological and Biomedical Ontology (OBO) Foundry ontologies that adheres to the Basic Formal Ontology (BFO)’s framework. In this step, the EBP, PBE, and statistic ontologies are developed independently. Next, the study integrates these individual ontologies into the final ACLR Ontology (ACLRO) as a more meaningful model that endorses the reusability and the ease of the model-expansion process since the classes can grow independently from one another. Finally, the study employs a use case and DL queries for model validation. The study's innovation is to present the ontology implementation for best-practice medicine and demonstrate how it can be applied to a real-world setup with semantic information. The ACLRO simultaneously emphasizes knowledge representation in health-intervention, statistics, research design, and external research evidence, while constructing the classes of data-driven and patient-focus processes that allow knowledge sharing explicit of technology. Additionally, the model synthesizes multiple related ontologies, which leads to the successful application of best-practice medicine.

Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms at their two terminals. In this paper, we investigate spike-timing dependent plasticity (STDP) with a single pairing of one presynaptic voltage spike and one postsynaptic voltage spike in a BiFeO3 memristive device. In most memristive materials the learning window is primarily a function of the material characteristics and not of the applied waveform. In contrast, we show that the analog resistive switching of the developed artificial synapses allows to adjust the learning time constant of the STDP function from 25ms to 125μs via the duration of applied voltage spikes. Also, as the induced weight change may degrade, we investigate the remanence of the resistance change for several hours after analog resistive switching, thus emulating the processes expected in biological synapses. As the power consumption is a major constraint in neuromorphic circuits, we show methods to reduce the consumed energy per setting pulse to only 4.5 pJ in the developed artificial synapses.

Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms at their two terminals. In this paper, we investigate spike-timing dependent plasticity (STDP) with a single pairing of one presynaptic voltage spike and one postsynaptic voltage spike in a BiFeO3 memristive device. In most memristive materials the learning window is primarily a function of the material characteristics and not of the applied waveform. In contrast, we show that the analog resistive switching of the developed artificial synapses allows to adjust the learning time constant of the STDP function from 25ms to 125μs via the duration of applied voltage spikes. Also, as the induced weight change may degrade, we investigate the remanence of the resistance change for several hours after analog resistive switching, thus emulating the processes expected in biological synapses. As the power consumption is a major constraint in neuromorphic circuits, we show methods to reduce the consumed energy per setting pulse to only 4.5 pJ in the developed artificial synapses.

The size reduction of transistors has been the main reason for a successful development of semiconductor integrated circuits over the last decades. Because of the physically limited downscaling of transistors, alternative technologies namely the information processing and nonvolatile resistive switches (also termed memristors) have come into focus. Memristors reveal a fast switching speed, long retention time, and stable endurance. Nonvolatile analog bipolar resistive switching with a considerable large On/Off ratio is reported in BiFeO3 (BFO)-based resistive switches. So far resistive switches are mainly applied in memory applications or logic operations. Given the excellent properties of BFO based memristors, the further exploration of functionalities for memristive devices is required. A new approach for hardware based cryptographic system was developed within the framework of this dissertation. By studying the power conversion efficiencies on BFO memristor at various harmonics, it has been shown that two sets of clearly distinguishable power ratios are achievable when the BFO memristor is set into high or into low resistance state. Thus, a BFO-based binary encoding system can be established. As an example the unrecoverable seizure information from encoded medical data suggests the proper functioning of the proposed encryption system. Aside from cryptographic functionality, the single pairing spike timing dependent plasticity (STDP) in BFO-based artificial synapses is demonstrated, which can be considered as the cornerstone for energy-efficient and fast hardware-based neuromorphic networks. In comparison to the biological driven realistic way, only single one pairing of pre- and postsynaptic spikes is applied to the BFO-based artificial synapse instead of 60-80 pairings. Thus, the learning time constant of STDP function can be reduced from 25 ms to 125 us. / In den letzten Jahrzehnten war die Größenreduktion von Transistoren einer der Hauptgründe für die Leistungssteigerung von integrierten Halbleiterschaltungen. Aufgrund des physikalisch beschränkten Skalierungspotentials, werden alternative Technologien für Halbleiterschaltungen entwickelt. Dazu zählen neuartige Widerstandsschalter, sogenannte Memristoren, welche wegen ihrer schnellen Schaltgeschwindigkeit, langen Speicherzeit und stabilen Haltbarkeit in den Fokus der Forschung gerückt sind. Das nichtflüchtige analoge bipolare Schalten des Widerstandwertes mit einem On/Off Verhältnis größer als 100 wurde in BiFeO 3 (BFO)-basierten Widerstands-schaltern beobachtet. Bisher wurden Widerstandsschalter hauptsächlich als Speicher oder in rekonfigurierbaren Logikschaltungen verwendet. Aufgrund der ausgezeichneten Eigenschaften von BFO-basierten Memristoren, ist die Untersuchung weiterer neuer Funktionalitäten vielversprechend. Als neuer Ansatz für ein Hardware-basiertes Kryptosystem wird in der vorliegenden Arbeit die Ausnutzung des Leistungsübertragungskoeffizienten in BFO Memristoren vorgeschlagen. Mit Hilfe der unterschiedlichen Oberschwingungen, welche von einem BFO Memristor im ON und OFF Zustand generiert werden, wurde ein Kryptosystem zum Kodieren binärer Daten entwickelt. Ein Test des Hardware-basierten Kryptosystems an Biodaten ergab, dass die kodierten Biodaten keine vorhersagbare Korrelation mehr enthielten. In der vorliegenden Arbeit wurden darüberhinaus BFO-basierte künstliche Synapsen mit einer Aktionspotentials-Intervall abhängigen Plastizität (STDP) für Einzelpulse entwickelt. Diese Einzelpuls-STDP legt den Grundstein für energieffiziente und schnelle neuromorphe Netzwerke mit künstlichen Synapsen. Im Vergleich zu biologischen Synapsen mit einer 60-80-Puls-STDP und einem Lernfenster auf der ms-Zeitskale, konnte das Lernfenster von BFO-basierten künstlichen Synapsen von 25 ms auf 125 μs reduziert werden. Solch ein schnelles Lernen ermöglicht auch die extreme Reduzierung des Leistungsverbrauchs in neuromorphen Netzwerken.

The size reduction of transistors has been the main reason for a successful development of semiconductor integrated circuits over the last decades. Because of the physically limited downscaling of transistors, alternative technologies namely the information processing and nonvolatile resistive switches (also termed memristors) have come into focus. Memristors reveal a fast switching speed, long retention time, and stable endurance. Nonvolatile analog bipolar resistive switching with a considerable large On/Off ratio is reported in BiFeO3 (BFO)-based resistive switches. So far resistive switches are mainly applied in memory applications or logic operations. Given the excellent properties of BFO based memristors, the further exploration of functionalities for memristive devices is required. A new approach for hardware based cryptographic system was developed within the framework of this dissertation. By studying the power conversion efficiencies on BFO memristor at various harmonics, it has been shown that two sets of clearly distinguishable power ratios are achievable when the BFO memristor is set into high or into low resistance state. Thus, a BFO-based binary encoding system can be established. As an example the unrecoverable seizure information from encoded medical data suggests the proper functioning of the proposed encryption system. Aside from cryptographic functionality, the single pairing spike timing dependent plasticity (STDP) in BFO-based artificial synapses is demonstrated, which can be considered as the cornerstone for energy-efficient and fast hardware-based neuromorphic networks. In comparison to the biological driven realistic way, only single one pairing of pre- and postsynaptic spikes is applied to the BFO-based artificial synapse instead of 60-80 pairings. Thus, the learning time constant of STDP function can be reduced from 25 ms to 125 us. / In den letzten Jahrzehnten war die Größenreduktion von Transistoren einer der Hauptgründe für die Leistungssteigerung von integrierten Halbleiterschaltungen. Aufgrund des physikalisch beschränkten Skalierungspotentials, werden alternative Technologien für Halbleiterschaltungen entwickelt. Dazu zählen neuartige Widerstandsschalter, sogenannte Memristoren, welche wegen ihrer schnellen Schaltgeschwindigkeit, langen Speicherzeit und stabilen Haltbarkeit in den Fokus der Forschung gerückt sind. Das nichtflüchtige analoge bipolare Schalten des Widerstandwertes mit einem On/Off Verhältnis größer als 100 wurde in BiFeO 3 (BFO)-basierten Widerstands-schaltern beobachtet. Bisher wurden Widerstandsschalter hauptsächlich als Speicher oder in rekonfigurierbaren Logikschaltungen verwendet. Aufgrund der ausgezeichneten Eigenschaften von BFO-basierten Memristoren, ist die Untersuchung weiterer neuer Funktionalitäten vielversprechend. Als neuer Ansatz für ein Hardware-basiertes Kryptosystem wird in der vorliegenden Arbeit die Ausnutzung des Leistungsübertragungskoeffizienten in BFO Memristoren vorgeschlagen. Mit Hilfe der unterschiedlichen Oberschwingungen, welche von einem BFO Memristor im ON und OFF Zustand generiert werden, wurde ein Kryptosystem zum Kodieren binärer Daten entwickelt. Ein Test des Hardware-basierten Kryptosystems an Biodaten ergab, dass die kodierten Biodaten keine vorhersagbare Korrelation mehr enthielten. In der vorliegenden Arbeit wurden darüberhinaus BFO-basierte künstliche Synapsen mit einer Aktionspotentials-Intervall abhängigen Plastizität (STDP) für Einzelpulse entwickelt. Diese Einzelpuls-STDP legt den Grundstein für energieffiziente und schnelle neuromorphe Netzwerke mit künstlichen Synapsen. Im Vergleich zu biologischen Synapsen mit einer 60-80-Puls-STDP und einem Lernfenster auf der ms-Zeitskale, konnte das Lernfenster von BFO-basierten künstlichen Synapsen von 25 ms auf 125 μs reduziert werden. Solch ein schnelles Lernen ermöglicht auch die extreme Reduzierung des Leistungsverbrauchs in neuromorphen Netzwerken.

Avec le développement des circuits intégrés à très faible consommation d'énergie, la nécessité de réduire les coûts d'exploitation des dispositifs électroniques embarqués et l'utilisation des piles usagées constituant une menace pour l'environnement, le concept de récupération d'énergie a acquis un nouvel intérêt. La récupération d'énergie couvre le piégeage de nombreuses sources d'énergie ambiantes perdues et leur conversion en énergie électrique. Une large gamme de dispositifs de récupération d'énergie des vibrations mécaniques a été développée. Une configuration commune consiste en un système de masse-ressort avec un matériau piézoélectrique en parallèle avec le ressort pour convertir une partie de l'énergie mécanique pendant les oscillations en énergie électrique. Jusqu'à présent, le matériau le plus utilisé pour la récupération d'énergie piézoélectrique est le titano-zirconate de plomb (PbZr1-xTixO3) (PZT). Le PZT est le matériau de référence pour les applications microsystème électromécanique-MEMS (MechanoElectroMechanicalSystems) dans le domaine de la récupération d'énergie. Les matériaux piézoélectriques à base de plomb tels que le PZT et niobate-titanate de plomb-magnésium (PMN-PT) offrent des facteurs de couplage piézoélectriques supérieurs à ceux d'autres matériaux. Cependant, malgré ses excellentes propriétés électriques (diélectriques, ferroélectriques et piézoélectriques), le PZT et d'autres matériaux à base de plomb devraient bientôt être remplacés par des composés sans plomb, à cause des problèmes environnementaux. Notre travail vise à développer des matériaux sans plomb de haute performance pour la récupération d'énergie par vibration mécanique. Nous nous sommes intéressés à la fabrication et la caractérisation des dispositifs MEMS pour la récupération d'énergie en utilisant les matériaux piézoélectriques sans plomb tels que le nitrure d'aluminium (AIN), le titanate de baryum BaTiO3 (BTO) et la ferrite de bismuth BiFeO3 (BFO). Les matériaux piézoélectriques PZT (utilisé comme référence à cause ses coefficients piézoélectriques élevés), BTO, BFO et AIN ont été déposés en utilisant des méthodes de dépôt telles que la pulvérisation cathodique et le dépôt sol-gel, conduisant à des films minces à grande échelle, homogènes et de haute densité, avec une épaisseur contrôlée avec précision. Le dépôt de films de 300 nm d'épaisseur par pulvérisation cathodique ou par Sol-Gel a été réalisé sur du substrat de SrTiO3 (STO) recouvert d'une électrode inférieure de SrRuO3 (SRO), qui est le substrat de référence pour les oxydes fonctionnels (PZT, BTO et BFO), et sur un substrat de silicium recouvert de platine, qui est le modèle industriel classique. Quels que soient les matériaux piézoélectriques, nous avons obtenu des films épitaxiés sur substrat de STO et texturés sur substrat de silicium. Des mesures structurales, électriques et piézoélectriques sur les films de BTO, AIN et PZT montrent qu'ils ont de bonnes propriétés physiques en accord avec la littérature. / With the development of ultra-low-power integrated circuits, the need to reduce operating costs for embedded electronic devices, and since used batteries pose a threat to the environment, the concept of energy harvesting has gained a new relevance. Energy harvesting covers the scavenging of many lost ambient energy sources and their conversion into electrical energy. A broad range of energy harvesting devices has been developed to scavenge energy from mechanical vibrations. A common configuration consists of a spring-mass system with a piezoelectric material in parallel with the spring to convert some of the mechanical energy during oscillations into electrical power. So far the most used material for piezoelectric energy harvesting is the Lead Zirconate Titanate (PbZr1-xTixO3) (PZT). PZT is the reference material for MEMS (MechanoElectroMechanicalSystems) applications in the field of energy harvesting. Lead-based piezoelectric materials such as PZT and lead magnesium niobate-lead titanate (PMN-PT) offer incomparable piezoelectric coupling factors to other materials. However, despite its excellent electrical properties (dielectric, ferroelectric and piezoelectric), PZT and other Lead based materials should be replaced shortly by leadfree compounds, due to environmental issues. Our work aims at developing lead-free high performance vibration energy-harvesting. We focus on the fabrication and characterization of aluminum nitride (AlN), Barium titanate BaTiO3 (BTO) and Bismuth ferrite BiFeO3 (BFO) devices for energy harvesting. PZT (as a reference because it’s high piezoelectric coefficients), BTO, BFO and AlN have been deposited using sputtering methods, leading to high homogeneous, large scale thin films with a precisely controlled thickness. The deposition of 300nm-thick films by sputtering or spin coating was performed on SrTiO3 (STO) substrate with SrRuO3 (SRO) bottom electrode, which is the reference substrate for the functional oxides (PZT, BTO and BFO), and platinum coated silicon substrate, which is the classic industrial template. Whatever the piezoelectric materials, we obtained epitaxial films on STO substrate and textured films on silicon substrate. Structural, electrical and piezoelectric measurements on the BTO, AlN and PZT films show that they have good physical properties in agreement with the literature.

Bismuth ferrite (BFO) is a multiferroic material with cross-correlation between magnetic and electric orders. With no applied external fields the spin structure of BFO is anitferromagnetic and cycloidal. This ordering prevents the detection of the weak ferromagnetism known to exist in the material. The application of magnetic and electric fields of suitable strength and direction is capable of compelling the Fe3+ spins to align in a homogeneous, antiferromagnetic fashion. This report details how numerical methods were used to simulate the spin alignment of a BFO system under different fields. The results were compiled into electric field-magnetic field phase diagrams of BFO to show the divide between cycloidal and homogeneous systems. / Graduate / 0607 / 0611 / marca@uvic.ca

Over the last two decades, great progress has been made in the understanding of multiferroic materials, ones where multiple long-range orders simultaneously exist. However, much of the research has focused on bulk systems. If these materials are to be incorporated into devices, they would not be in bulk form, but would be miniaturized, such as in nanoparticle form. Accordingly, a better understanding of multiferroic nanoparticles is necessary. This manuscript examines the multiferroic phase diagram of multiferroic nanoparticles related to system size and surface-induced magnetic anisotropy. There is a particular focus on bismuth ferrite, the room-temperature antiferromagnetic-ferroelectric multiferroic. Theoretical results will be presented which show that at certain sizes, a bistability develops in the cycloidal wavevector. This implies bistability in the ferroelectric and magnetic moments of the nanoparticles. This novel magnetoelectric bistability may be of use in the creation of an electrically-written, magnetically-read memory element. / Graduate