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.

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).

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.

The realization of Silicon based photonic devices will enable much faster data transmission than is possible today using the current electronics based devices. Group IV alloys germanium tin (GeSn) and silicon germanium tin (SiGeSn) have the potential to form an direct bandgap material and thus, they are promising candidates to develop a Si compatible light source and advance the field of silicon photonics. However, the growth of the alloys is challenging as it requires low temperature growth and proper strain management in the films during growth to prevent tin segregation. In order to satisfy these criteria, various research groups have developed novel chemical vapor deposition (CVD) reactors to deposit the films. While these reactors have been highly successful in depositing high crystal quality high Sn concentration films, they are generally expensive set-ups which utilize several turbomolecular/cryogenic pumps and/or load-lock systems. An more economical process than the state-of-the art to grow group IV materials will be highly valuable. Thus, the work presented in this dissertation was focused on deposition of group IV semiconductor thin films using simplified plasma enhanced CVD (PECVD) reactors.

Two different in-house assembled PECVD reactor systems, namely Reactor No. 1 and 2, were utilized to deposit Ge, GeSn and SiGeSn thin films. PECVD technique was used as plasma assistance allows for potentially depositing the films at growth temperatures lower than those of conventional CVD. Germane (GeH4) and Digermane (Ge2H6) were used as the Ge precursor while Disilane (Si2H6) and tin chloride (SnCl4) were used as the precursors for Si and Sn respectively. The growth conditions such as growth temperature, precursor flow rates, precursor partial pressures, and chamber pressure were varied in a wide range to optimize the growth conditions for the films. Polycrystalline Ge films and SiGeSn films with an Sn content upto 8% were deposited using Reactor No. 1 and 2. Development of epitaxial Ge buffers and GeSn films was accomplished using a modified Reactor No. 2 at temperatures <400oC without the aid of ultra-high vacuum conditions or a high temperature substrate pre-deposition bake thereby leading to a low economic and thermal budget for the deposition process.

It is well known that radiative heat transfer rate can exceed that between two blackbodies by several orders of magnitude due to the coupling of evanescent waves. One promising application of near-field thermal radiation is thermophotovoltaic (TPV) devices, which convert thermal energy to electricity. Recently, different types of metamaterials with excitations of surface plasmon polaritons (SPPs)/surface phonon polaritons (SPhPs), magnetic polaritons (MP), and hyperbolic modes (HM), have been studied to further improve near-field radiative heat flux and conversion efficiency. On the other hand, near-field experimental demonstration between planar surfaces has been limited due to the extreme challenge in the vacuum gap control as well as the parallelism.

The main objective of this work is to experimentally study the near-field radiative transfer and the excitation of resonance modes by designing nanostructured thin films separated by nanometer vacuum gaps. In particular, the near-field radiative heat transfer between two parallel plates of intrinsic silicon wafers coated with a thin film of aluminum nanostructure is investigated. In addition, theoretical studies about the effects of different physical mechanisms such as SPhP/SPP, MPs, and HM on near-field radiative transfer in various nanostructured metamaterials are conducted particularly for near-field TPV applications. Numerical simulations are performed by using multilayer transfer matrix method, rigorous coupled wave analysis, and finite difference time domain techniques incorporated with fluctuational electrodynamics. The understanding gained here will undoubtedly benefit the spectral control of near-field thermal radiation for energy-harvesting applications like thermophotovoltaic energy conversion and radiation-based thermal management.

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.

This paper will primarily deal with obstacle detection and the benefits that radar technology provides as the primary interface. The concept that is being proposed involves using a non-industrialized radar to achieve similar results when trying to detect a present object. By being able to achieve a working radar detection system at a more general domain, the path to it becoming more universal accessible increases. This, in turn, will hopefully amplify the areas in which radar technology can be applied to and lead to great benefits universally. From the compiled data and the work that has been done to achieve a responsive radar, it is noted that the radar will provide an accurate reading in most conditions that it is introduced to. These conditions vary from range resolution aspects to various weather environments, as well as the visibility aspect. However, based on the results that were achieved, through various testing, there are still some areas in which radar technology needs to improve in, for it to be fully considered as the sole interface when it comes to obstacle detection and its integration into future technology like self-driving cars. Nevertheless, the capabilities of radar technology at this caliber is noted to be quite impressive and similar to other more expansive options that are available.

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.

The study of soft magnetic materials has been growing in popularity in recent years. Driving this interest are new applications for traditional electrical power-management components, such as inductors and transformers, which must be scaled down to the micro and nano scale while the frequencies of operation have been scaling up to the gigahertz range and beyond. The exceptional magnetic properties of the materials make them highly effective in these small-component applications, but the ability of these materials to provide highly-effective shielding has not been so thoroughly considered. Most shielding is done with traditional metals, such as aluminum, because of the relatively low cost of the material and high workability in shaping the material to meet size and dimensional requirements.

This research project focuses on analyzing the variance in shielding effectiveness and electromagnetic field effects of a thin film of Cobalt Zirconium Tantalum Boron (CZTB) in the band of frequencies most likely to require innovative solutions to long-standing problems of noise and interference. The measurements include Near H-Field attenuation and field effects, Far Field shielding, and Backscatter. Minor variances in the thickness and layering of sputter deposition can have significant changes electromagnetic signature of devices which radiate energy through the material.

The material properties presented in this research are H-Field attenuation, H-Field Flux Orientation, Far-Field Approximation, E Field Vector Directivity, H Field Vector Directivity, and Backscatter Magnitude. The results are presented, analyzed and explained using characterization techniques. Future work includes the effect of sputter deposition orientation, application to devices, and applicability in mitigating specific noise signals beyond the 5G band.

A new type of electronics was envisioned, namely edible electronics. Edible electronics are made by Food and Drug Administration (FDA) certified edible materials which can be eaten and digested by human body. Different from implantable electronics, test or treatment using edible electronics doesn’t require operations and perioperative complications.

This dissertation bridges the food industry, material sciences, device fabrication, and biomedical engineering by demonstrating edible supercapacitors and electronic components and devices such as pH sensor.

Edible supercapacitors were fabricated using food materials from grocery store. 5 of them were connected in series to power a snake camera. Tests result showed that the current generated by supercapacitor have the ability to kill bacteria. Next more food, processed food and non-toxic level electronic materials were investigated. A “preferred food kit” was created for component fabrication based on the investigation. Some edible electronic components, such as wires, resistor, inductor, etc., were developed and characterized utilizing the preferred food kit. These components make it possible to fabricate edible electronic/device in the future work. Some edible electronic components were integrated into an edible electronic system/device. Then edible pH sensor was introduced and fabricated. This edible pH sensor can be swallowed and test pH of gastric fluid. PH can be read in a phone within seconds after the pH sensor was swallowed. As a side project, an edible double network gel electrolyte was synthesized for the edible supercapacitor.