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- All Subjects: Electrical Engineering
- Creators: Yu, Hongbin
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Description
Patterning technologies for micro/nano-structures have been essentially used in a variety of discipline research areas, including electronics, optics, material science, and biotechnology. Therefore their importance has dramatically increased over the past decades. This dissertation presents various advanced patterning processes utilizing cross-discipline technologies, e.g., photochemical deposition, transfer printing (TP), and nanoimprint lithography (NIL), to demonstrate inexpensive, high throughput, and scalable manufacturing for advanced optical applications.
The polymer-assisted photochemical deposition (PPD) method is employed in the form of additive manufacturing (AM) to print ultra-thin (< 5 nm) and continuous film in micro-scaled (> 6.5 μm) resolution. The PPD film acts as a lossy material in the Fabry-Pérot cavity structures and generates vivid colored images with a micro-scaled resolution by inducing large modulation of reflectance. This PPD-based structural color printing performs without photolithography and vacuum deposition in ambient and room-temperature conditions, which enables an accessible and inexpensive process (Chapter 1).
In the TP process, germanium (Ge) is used as the nucleation layer of noble metallic thin films to prevent structural distortion and improve surface morphology. The developed Ge-assisted transfer printing (GTP) demonstrates its feasibility transferring sub-100 nm features with up to 50 nm thickness in a centimeter scale. The GTP is also capable of transferring arbitrary metallic nano-apertures with minimal pattern distortion, providing relatively less expensive, simpler, and scalable manufacturing (Chapter 2).
NIL is employed to fabricate the double-layered chiral metasurface for polarimetric imaging applications. The developed NIL process provides multi-functionalities with a single NIL, i.e., spacing layer, planarized surface, and formation of dielectric gratings, respectively, which significantly reduces fabrication processing time and potential cost by eliminating several steps in the conventional fabrication process. During the integration of two metasurfaces, the Moiré fringe based alignment method is employed to accomplish the alignment accuracy of less than 200 nm in both x- and y-directions, which is superior to conventional photolithography. The dramatically improved optical performance, e.g., highly improved circular polarization extinction ratio (CPER), is also achieved with the developed NIL process (Chapter 3).
ContributorsChoi, Shinhyuk (Author) / Wang, Chao (Thesis advisor) / Yu, Hongbin (Committee member) / Holman, Zachary (Committee member) / Hwa, Yoon (Committee member) / Arizona State University (Publisher)
Created2023
Description
Graphene has been extensively researched for both scientific and technological interests since its first isolation from graphite. The excellent transport properties and long spin diffusion length of graphene make it a promising material for electronic and spintronic device applications. This dissertation deals with the optimization of magnetic field sensing in graphene and the realization of nanoparticle induced ferromagnetism in graphene towards spintronic device applications.
Graphene has been used as a channel material for magnetic sensors demonstrating the potential for very high sensitivities, especially for Hall sensors, due to its extremely high mobility and low carrier concentration. However, the two-carrier nature of graphene near the charge neutrality point (CNP) causes a nonlinearity issue for graphene Hall sensors, which limits useful operating ranges and has not been fully studied. In this dissertation, a two-channel model was used to describe the transport of graphene near the CNP. The model was carefully validated by experiments and then was used to explore the optimization of graphene sensor performance by tuning the gate operating bias under realistic constraints on linearity and power dissipation.
The manipulation of spin in graphene that is desired for spintronic applications is limited by its weak spin-orbit coupling (SOC). Proximity induced ferromagnetism (PIFM) from an adjacent ferromagnetic insulator (FMI) provides a method for enhancing SOC in graphene without degrading its transport properties. However, suitable FMIs are uncommon and difficult to integrate with graphene. In this dissertation, PIFM in graphene from an adjacent Fe3O4 magnetic nanoparticle (MNP) array was demonstrated for the first time. Observation of the anomalous Hall effect (AHE) in the device structures provided the signature of PIFM. Comparison of the test samples with different control samples conclusively proved that exchange interaction at the MNP/graphene interface was responsible for the observed characteristics. The PIFM in graphene was shown to persist at room temperature and to be gate-tunable, which are desirable features for electrically controlled spintronic device applications.
The observation of PIFM in the MNP/graphene devices indicates that the spin transfer torque (STT) from spin-polarized current in the graphene can interact with the magnetization of the MNPs. If there is sufficient STT, spin torque oscillation (STO) could be realized in this structure. In this dissertation, three methods were employed to search for signatures of STO in the devices. STO was not observed in our devices, most likely due to the weak spin-polarization for current injected from conventional ferromagnetic contacts to graphene. Calculation indicates that graphene should provide sufficient spin-polarized current for exciting STO in optimized structures that miniaturize the device area and utilize optimized tunnel-barrier contacts for improved spin injection.
Graphene has been used as a channel material for magnetic sensors demonstrating the potential for very high sensitivities, especially for Hall sensors, due to its extremely high mobility and low carrier concentration. However, the two-carrier nature of graphene near the charge neutrality point (CNP) causes a nonlinearity issue for graphene Hall sensors, which limits useful operating ranges and has not been fully studied. In this dissertation, a two-channel model was used to describe the transport of graphene near the CNP. The model was carefully validated by experiments and then was used to explore the optimization of graphene sensor performance by tuning the gate operating bias under realistic constraints on linearity and power dissipation.
The manipulation of spin in graphene that is desired for spintronic applications is limited by its weak spin-orbit coupling (SOC). Proximity induced ferromagnetism (PIFM) from an adjacent ferromagnetic insulator (FMI) provides a method for enhancing SOC in graphene without degrading its transport properties. However, suitable FMIs are uncommon and difficult to integrate with graphene. In this dissertation, PIFM in graphene from an adjacent Fe3O4 magnetic nanoparticle (MNP) array was demonstrated for the first time. Observation of the anomalous Hall effect (AHE) in the device structures provided the signature of PIFM. Comparison of the test samples with different control samples conclusively proved that exchange interaction at the MNP/graphene interface was responsible for the observed characteristics. The PIFM in graphene was shown to persist at room temperature and to be gate-tunable, which are desirable features for electrically controlled spintronic device applications.
The observation of PIFM in the MNP/graphene devices indicates that the spin transfer torque (STT) from spin-polarized current in the graphene can interact with the magnetization of the MNPs. If there is sufficient STT, spin torque oscillation (STO) could be realized in this structure. In this dissertation, three methods were employed to search for signatures of STO in the devices. STO was not observed in our devices, most likely due to the weak spin-polarization for current injected from conventional ferromagnetic contacts to graphene. Calculation indicates that graphene should provide sufficient spin-polarized current for exciting STO in optimized structures that miniaturize the device area and utilize optimized tunnel-barrier contacts for improved spin injection.
ContributorsSong, Guibin (Author) / Kiehl, Richard A. (Committee member) / Yu, Hongbin (Committee member) / Chen, Tingyong (Committee member) / Rizzo, Nicholas D (Committee member) / Arizona State University (Publisher)
Created2019
Description
Transportation plays a significant role in every human's life. Numerous factors, such as cost of living, available amenities, work style, to name a few, play a vital role in determining the amount of travel time. Such factors, among others, led in part to an increased need for private transportation and, consequently, leading to an increase in the purchase of private cars. Also, road safety was impacted by numerous factors such as Driving Under Influence (DUI), driver’s distraction due to the increase in the use of mobile devices while driving. These factors led to an increasing need for an Advanced Driver Assistance System (ADAS) to help the driver stay aware of the environment and to improve road safety.
EcoCAR3 is one of the Advanced Vehicle Technology Competitions, sponsored by the United States Department of Energy (DoE) and managed by Argonne National Laboratory in partnership with the North American automotive industry. Students are challenged beyond the traditional classroom environment in these competitions, where they redesign a donated production vehicle to improve energy efficiency and to meet emission standards while maintaining the features that are attractive to the customer, including but not limited to performance, consumer acceptability, safety, and cost.
This thesis presents a driver assistance system interface that was implemented as part of EcoCAR3, including the adopted sensors, hardware and software components, system implementation, validation, and testing. The implemented driver assistance system uses a combination of range measurement sensors to determine the distance, relative location, & the relative velocity of obstacles and surrounding objects together with a computer vision algorithm for obstacle detection and classification. The sensor system and vision system were tested individually and then combined within the overall system. Also, a visual and audio feedback system was designed and implemented to provide timely feedback for the driver as an attempt to enhance situational awareness and improve safety.
Since the driver assistance system was designed and developed as part of a DoE sponsored competition, the system needed to satisfy competition requirements and rules. This work attempted to optimize the system in terms of performance, robustness, and cost while satisfying these constraints.
EcoCAR3 is one of the Advanced Vehicle Technology Competitions, sponsored by the United States Department of Energy (DoE) and managed by Argonne National Laboratory in partnership with the North American automotive industry. Students are challenged beyond the traditional classroom environment in these competitions, where they redesign a donated production vehicle to improve energy efficiency and to meet emission standards while maintaining the features that are attractive to the customer, including but not limited to performance, consumer acceptability, safety, and cost.
This thesis presents a driver assistance system interface that was implemented as part of EcoCAR3, including the adopted sensors, hardware and software components, system implementation, validation, and testing. The implemented driver assistance system uses a combination of range measurement sensors to determine the distance, relative location, & the relative velocity of obstacles and surrounding objects together with a computer vision algorithm for obstacle detection and classification. The sensor system and vision system were tested individually and then combined within the overall system. Also, a visual and audio feedback system was designed and implemented to provide timely feedback for the driver as an attempt to enhance situational awareness and improve safety.
Since the driver assistance system was designed and developed as part of a DoE sponsored competition, the system needed to satisfy competition requirements and rules. This work attempted to optimize the system in terms of performance, robustness, and cost while satisfying these constraints.
ContributorsBalaji, Venkatesh (Author) / Karam, Lina J (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Flexible conducting materials have been in the forefront of a rapidly transforming electronics industry, focusing on wearable devices for a variety of applications in recent times. Over the past few decades, bulky, rigid devices have been replaced with a surging demand for thin, flexible, light weight, ultra-portable yet high performance electronics. The interconnects available in the market today only satisfy a few of the desirable characteristics, making it necessary to compromise one feature over another. In this thesis, a method to prepare a thin, flexible, and stretchable inter-connect is presented with improved conductivity compared to previous achievements. It satisfies most mechanical and electrical conditions desired in the wearable electronics industry. The conducting composite, prepared with the widely available, low cost silicon-based organic polymer - polydimethylsiloxane (PDMS) and silver (Ag), is sandwiched between two cured PDMS layers. These protective layers improve the mechanical stability of the inter-connect. The structure can be stretched up to 120% of its original length which can further be enhanced to over 250% by cutting it into a serpentine shape without compromising its electrical stability. The inter-connect, around 500 µm thick, can be integrated into thin electronic packaging. The synthesis process of the composite material, along with its electrical and mechanical and properties are presented in detail. Testing methods and results for mechanical and electrical stability are also illustrated over extensive flexing and stretching cycles. The materials put into test, along with conductive silver (Ag) - polydimethylsiloxane (PDMS) composite in a sandwich structure, are copper foils, copper coated polyimide (PI) and aluminum (Al) coated polyethylene terephthalate (PET).
ContributorsNandy, Mayukh (Author) / Yu, Hongbin (Thesis advisor) / Chan, Candace (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2020
Description
Though a single mode of energy transfer, optical radiation meaningfully interacts with its surrounding environment at over a wide range of physical length scales. For this reason, its reconstruction and measurement are of great importance in remote sensing, as these multi-scale interactions encode a great deal of information about distant objects, surfaces, and physical phenomena. For some remote sensing applications, obtaining a desired quantity of interest does not necessitate the explicit mapping of each point in object space to an image space with lenses or mirrors. Instead, only edge rays or physical boundaries of the sensing instrument are considered, while the spatial intensity distribution of optical energy received from a distant object informs its position, optical characteristics, or physical/chemical state.
Admittedly specialized, the principals and consequences of non-imaging optics are nevertheless applicable to heterogeneous semiconductor integration and automotive light detection and ranging (LiDAR), two important emerging technologies. Indeed, a review of relevant engineering literature finds two under-addressed remote sensing challenges. The semiconductor industry lacks an optical strain metrology with displacement resolution smaller than 100 nanometers capable of measuring strain fields between high-density interconnect lines. Meanwhile, little attention is paid to the per-meter sensing characteristics of scene-illuminating flash LiDAR in the context of automotive applications, despite the technology’s much lower cost. It is here that non-imaging optics offers intriguing instrument design and explanations of observed sensor performance at vastly different length scales.
In this thesis, an effective non-contact technique for mapping nanoscale mechanical strain fields and out-of-plane surface warping via laser diffraction is demonstrated, with application as a novel metrology for next-generation semiconductor packages. Additionally, object detection distance of low-cost automotive flash LiDAR, on the order of tens of meters, is understood though principals of optical energy transfer from the surface of a remote object to an extended multi-segment detector. Such information is of consequence when designing an automotive perception system to recognize various roadway objects in low-light scenarios.
Admittedly specialized, the principals and consequences of non-imaging optics are nevertheless applicable to heterogeneous semiconductor integration and automotive light detection and ranging (LiDAR), two important emerging technologies. Indeed, a review of relevant engineering literature finds two under-addressed remote sensing challenges. The semiconductor industry lacks an optical strain metrology with displacement resolution smaller than 100 nanometers capable of measuring strain fields between high-density interconnect lines. Meanwhile, little attention is paid to the per-meter sensing characteristics of scene-illuminating flash LiDAR in the context of automotive applications, despite the technology’s much lower cost. It is here that non-imaging optics offers intriguing instrument design and explanations of observed sensor performance at vastly different length scales.
In this thesis, an effective non-contact technique for mapping nanoscale mechanical strain fields and out-of-plane surface warping via laser diffraction is demonstrated, with application as a novel metrology for next-generation semiconductor packages. Additionally, object detection distance of low-cost automotive flash LiDAR, on the order of tens of meters, is understood though principals of optical energy transfer from the surface of a remote object to an extended multi-segment detector. Such information is of consequence when designing an automotive perception system to recognize various roadway objects in low-light scenarios.
ContributorsHoughton, Todd Kristopher (Author) / Yu, Hongbin (Thesis advisor) / Jiang, Hanqing (Committee member) / Jayasuriya, Suren (Committee member) / Zhang, Liang (Committee member) / Arizona State University (Publisher)
Created2020
Description
Object detection is an interesting computer vision area that is concerned with the detection of object instances belonging to specific classes of interest as well as the localization of these instances in images and/or videos. Object detection serves as a vital module in many computer vision based applications. This work focuses on the development of object detection methods that exhibit increased robustness to varying illuminations and image quality. In this work, two methods for robust object detection are presented.
In the context of varying illumination, this work focuses on robust generic obstacle detection and collision warning in Advanced Driver Assistance Systems (ADAS) under varying illumination conditions. The highlight of the first method is the ability to detect all obstacles without prior knowledge and detect partially occluded obstacles including the obstacles that have not completely appeared in the frame (truncated obstacles). It is first shown that the angular distortion in the Inverse Perspective Mapping (IPM) domain belonging to obstacle edges varies as a function of their corresponding 2D location in the camera plane. This information is used to generate object proposals. A novel proposal assessment method based on fusing statistical properties from both the IPM image and the camera image to perform robust outlier elimination and false positive reduction is also proposed.
In the context of image quality, this work focuses on robust multiple-class object detection using deep neural networks for images with varying quality. The use of Generative Adversarial Networks (GANs) is proposed in a novel generative framework to generate features that provide robustness for object detection on reduced quality images. The proposed GAN-based Detection of Objects (GAN-DO) framework is not restricted to any particular architecture and can be generalized to several deep neural network (DNN) based architectures. The resulting deep neural network maintains the exact architecture as the selected baseline model without adding to the model parameter complexity or inference speed. Performance results provided using GAN-DO on object detection datasets establish an improved robustness to varying image quality and a higher object detection and classification accuracy compared to the existing approaches.
In the context of varying illumination, this work focuses on robust generic obstacle detection and collision warning in Advanced Driver Assistance Systems (ADAS) under varying illumination conditions. The highlight of the first method is the ability to detect all obstacles without prior knowledge and detect partially occluded obstacles including the obstacles that have not completely appeared in the frame (truncated obstacles). It is first shown that the angular distortion in the Inverse Perspective Mapping (IPM) domain belonging to obstacle edges varies as a function of their corresponding 2D location in the camera plane. This information is used to generate object proposals. A novel proposal assessment method based on fusing statistical properties from both the IPM image and the camera image to perform robust outlier elimination and false positive reduction is also proposed.
In the context of image quality, this work focuses on robust multiple-class object detection using deep neural networks for images with varying quality. The use of Generative Adversarial Networks (GANs) is proposed in a novel generative framework to generate features that provide robustness for object detection on reduced quality images. The proposed GAN-based Detection of Objects (GAN-DO) framework is not restricted to any particular architecture and can be generalized to several deep neural network (DNN) based architectures. The resulting deep neural network maintains the exact architecture as the selected baseline model without adding to the model parameter complexity or inference speed. Performance results provided using GAN-DO on object detection datasets establish an improved robustness to varying image quality and a higher object detection and classification accuracy compared to the existing approaches.
ContributorsPrakash, Charan Dudda (Author) / Karam, Lina (Thesis advisor) / Abousleman, Glen (Committee member) / Jayasuriya, Suren (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2020
Description
Wide bandgap semiconductors, also known as WBG semiconductors are materials which have larger bandgaps than conventional semiconductors such as Si or GaAs. They permit devices to operate at much higher voltages, frequencies and temperatures. They are the key material used to make LEDs, lasers, radio frequency applications, military applications, and power electronics. Their intrinsic qualities make them promising for next-generation devices for general semiconductor use. Their ability to handle higher power density is particularly attractive for attempts to sustain Moore's law, as conventional technologies appear to be reaching a bottleneck. Apart from WBG materials, ultra-wide bandgap (UWBG) materials, such as Ga2O3, AlN, diamond, or BN, are also attractive since they have even more extreme properties. Although this field is relatively new, which still remains a lot of effort to study and investigate, people can still expect that these materials could be the main characters for more advanced applications in the near future.
In the dissertation, three topics with power devices made by WBG or UWBG semiconductors were introduced. In chapter 1, a generally background knowledge introduction is given. This helps the reader to learn current research focuses. In chapter 2, a comprehensive study of temperature-dependent characteristics of Ga2O3 SBDs with highly-doped substrate is demonstrated. A modified thermionic emission model over an inhomogeneous barrier with a voltage-dependent barrier height is investigated. Besides, the mechanism of surface leakage current is also discussed. These results are beneficial for future developments of low-loss β-Ga2O3 electronics and optoelectronics. In chapter 3, vertical GaN Schottky barrier diodes (SBDs) with floating metal rings (FMRs) as edge termination structures on bulk GaN substrates was introduced. This work represents a useful reference for the FMR termination design for GaN power devices. In chapter 4, AlGaN/GaN metal-insulator-semiconductor high electron mobility transistors (MISHEMTs) fabricated on Si substrates with a 10 nm boron nitride (BN) layer as gate dielectric was demonstrated. The material characterization was investigated by X-ray photoelectric spectroscopy (XPS) and UV photoelectron spectroscopy (UPS). And the gate leakage current mechanisms were also investigated by temperature-dependent current-voltage measurements.
Although still in its infancy, past and projected future progress of electronic designs will ultimately achieve this very goal that WBG and UWBG semiconductors will be indispensable for today and future’s science, technologies and society.
ContributorsYang, Tsung-Han (Author) / Zhao, Yuji (Thesis advisor) / Vasileska, Dragica (Committee member) / Yu, Hongbin (Committee member) / Nemanich, Robert (Committee member) / Arizona State University (Publisher)
Created2021
Description
Wurtzite (B, Ga, Al) N semiconductors, especially (Ga, Al) N material systems, demonstrate immense promises to boost the economic growth in the semiconductor industry that is approaching the end of Moore’s law. At the material level, their high electric field strength, high saturation velocity, and unique heterojunction polarization charge have enabled tremendous potentials for high power, high frequency, and photonic applications. With the availability of large-area bulk GaN substrates and high-quality epilayer on foreign substrates, the power conversion applications of GaN are now at the cusp of commercialization.Despite these encouraging advances, there remain two critical hurdles in GaN-based technology: selective area doping and hole-based p-channel devices. Current selective area doping methods are still immature and lead to low-quality lateral p-n junctions, which prevent the realization of advanced power transistors and rectifiers. The missing of hole-based p-channel devices hinders the development of GaN complementary integrated circuits.
This thesis comprehensively studied these challenges. The first part (chapter 2) researched the selective area doping by etch-then-regrow. A GaN-based vertical-channel junction field-effect transistors (VC-JFETs) was experimentally demonstrated by blanket regrowth and self-planarization. The devices’ electrical performances were characterized to understand the regrowth quality. The non-ideal factors during p-GaN regrowth were also discussed. The second part (chapter 3-5) systematically studied the application of the hydrogen plasma treatment process to change the p-GaN properties selectively. A novel GaN-based metal-insulator-semiconductor junction was demonstrated. Then a novel edge termination design with avalanche breakdown capability achieved in GaN power rectifiers is proposed. The last part (Chapter 6) demonstrated a GaN-based p-channel heterojunction field-effect transistor, with record low leakage, subthreshold swing, and a record high on/off ratio. In the end, some outlook and future work have also been proposed.
Although in infancy, the demonstrated etch-then-regrow and the hydrogen plasma treatment methods have the potential to ultimately solve the challenges in GaN and benefit the development of the wide-ultra-wide bandgap industry, technology, and society.
ContributorsYang, Chen (Author) / Zhao, Yuji (Thesis advisor) / Goodnick, Stephen (Committee member) / Yu, Hongbin (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2021
Description
Machine learning (ML) and deep learning (DL) has become an intrinsic part of multiple fields. The ability to solve complex problems makes machine learning a panacea. In the last few years, there has been an explosion of data generation, which has greatly improvised machine learning models. But this comes with a cost of high computation, which invariably increases power usage and cost of the hardware. In this thesis we explore applications of ML techniques, applied to two completely different fields - arts, media and theater and urban climate research using low-cost and low-powered edge devices. The multi-modal chatbot uses different machine learning techniques: natural language processing (NLP) and computer vision (CV) to understand inputs of the user and accordingly perform in the play and interact with the audience. This system is also equipped with other interactive hardware setups like movable LED systems, together they provide an experiential theatrical play tailored to each user. I will discuss how I used edge devices to achieve this AI system which has created a new genre in theatrical play. I will then discuss MaRTiny, which is an AI-based bio-meteorological system that calculates mean radiant temperature (MRT), which is an important parameter for urban climate research. It is also equipped with a vision system that performs different machine learning tasks like pedestrian and shade detection. The entire system costs around $200 which can potentially replace the existing setup worth $20,000. I will further discuss how I overcame the inaccuracies in MRT value caused by the system, using machine learning methods. These projects although belonging to two very different fields, are implemented using edge devices and use similar ML techniques. In this thesis I will detail out different techniques that are shared between these two projects and how they can be used in several other applications using edge devices.
ContributorsKulkarni, Karthik Kashinath (Author) / Jayasuriya, Suren (Thesis advisor) / Middel, Ariane (Thesis advisor) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this dissertation, I described my research on the growth and characterization of various nanostructures, such as nanowires, nanobelts and nanosheets, of different semiconductors in a Chemical Vapor Deposition (CVD) system.
In the first part of my research, I selected chalcogenides (such as CdS and CdSe) for a comprehensive study in growing two-segment axial nanowires and radial nanobelts/sheets using the ternary CdSxSe1-x alloys. I demonstrated simultaneous red (from CdSe-rich) and green (from CdS-rich) light emission from a single monolithic heterostructure with a maximum wavelength separation of 160 nm. I also demonstrated the first simultaneous two-color lasing from a single nanosheet heterostructure with a wavelength separation of 91 nm under sufficiently strong pumping power.
In the second part, I considered several combinations of source materials with different growth methods in order to extend the spectral coverage of previously demonstrated structures towards shorter wavelengths to achieve full-color emissions. I achieved this with the growth of multisegment heterostructure nanosheets (MSHNs), using ZnS and CdSe chalcogenides, via our novel growth method. By utilizing this method, I demonstrated the first growth of ZnCdSSe MSHNs with an overall lattice mismatch of 6.6%, emitting red, green and blue light simultaneously, in a single furnace run using a simple CVD system. The key to this growth method is the dual ion exchange process which converts nanosheets rich in CdSe to nanosheets rich in ZnS, demonstrated for the first time in this work. Tri-chromatic white light emission with different correlated color temperature values was achieved under different growth conditions. We demonstrated multicolor (191 nm total wavelength separation) laser from a single monolithic semiconductor nanostructure for the first time. Due to the difficulties associated with growing semiconductor materials of differing composition on a given substrate using traditional planar epitaxial technology, our nanostructures and growth method are very promising for various device applications, including but not limited to: illumination, multicolor displays, photodetectors, spectrometers and monolithic multicolor lasers.
In the first part of my research, I selected chalcogenides (such as CdS and CdSe) for a comprehensive study in growing two-segment axial nanowires and radial nanobelts/sheets using the ternary CdSxSe1-x alloys. I demonstrated simultaneous red (from CdSe-rich) and green (from CdS-rich) light emission from a single monolithic heterostructure with a maximum wavelength separation of 160 nm. I also demonstrated the first simultaneous two-color lasing from a single nanosheet heterostructure with a wavelength separation of 91 nm under sufficiently strong pumping power.
In the second part, I considered several combinations of source materials with different growth methods in order to extend the spectral coverage of previously demonstrated structures towards shorter wavelengths to achieve full-color emissions. I achieved this with the growth of multisegment heterostructure nanosheets (MSHNs), using ZnS and CdSe chalcogenides, via our novel growth method. By utilizing this method, I demonstrated the first growth of ZnCdSSe MSHNs with an overall lattice mismatch of 6.6%, emitting red, green and blue light simultaneously, in a single furnace run using a simple CVD system. The key to this growth method is the dual ion exchange process which converts nanosheets rich in CdSe to nanosheets rich in ZnS, demonstrated for the first time in this work. Tri-chromatic white light emission with different correlated color temperature values was achieved under different growth conditions. We demonstrated multicolor (191 nm total wavelength separation) laser from a single monolithic semiconductor nanostructure for the first time. Due to the difficulties associated with growing semiconductor materials of differing composition on a given substrate using traditional planar epitaxial technology, our nanostructures and growth method are very promising for various device applications, including but not limited to: illumination, multicolor displays, photodetectors, spectrometers and monolithic multicolor lasers.
ContributorsTurkdogan, Sunay (Author) / Ning, Cun Zheng (Thesis advisor) / Palais, Joseph C. (Committee member) / Yu, Hongbin (Committee member) / Mardinly, A. John (Committee member) / Arizona State University (Publisher)
Created2015