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Hardware implementations of artificial neural networks (ANNs) have become feasible due to the advent of persistent 2-terminal devices such as memristor, phase change memory, MTJs, etc. Hybrid memristor crossbar/CMOS systems have been studied extensively and demonstrated experimentally. In these circuits, memristors located at each cross point in a crossbar are, however, stacked on top of CMOS circuits using back end of line processing (BOEL), limiting scaling. Each neuron’s functionality is spread across layers of CMOS and memristor crossbar and thus cannot support the required connectivity to implement large-scale multi-layered ANNs. This work proposes a new fine-grained 3D integrated circuit technology for ANNs that is one of the first IC technologies for this purpose. Synaptic weights implemented with devices are incorporated in a uniform vertical nanowire template co-locating the memory and computation requirements of ANNs within each neuron. Novel 3D routing features are used for interconnections in all three dimensions between the devices enabling high connectivity without the need for special pins or metal vias. To demonstrate the proof of concept of this fabric, classification of binary images using a perceptron-based feed forward neural network is shown. Bottom-up evaluations for the proposed fabric considering 3D implementation of fabric components reveal up to 19x density, 1.2x power benefits when compared to 16nm hybrid memristor/CMOS technology.

High defect rates are associated with novel nanodevice-based systems owing to unconventional and self-assembly based manufacturing processes. Furthermore, in emerging nanosystems, fault mechanisms and distributions may be very different from CMOS due to unique physical layer aspects, and emerging circuit and logic styles. Thus, theoretical fault models for nanosystems are necessary to extract detailed characteristics of fault generation and propagation. Using the intuition garnered from the theoretical analysis, modular and structural redundancy schemes can be specifically tailored to the intricacies of the fabric in order to achieve higher reliability of output signals. In this thesis, we develop a detailed analytical fault model for the Nanoscale Application Specific Integrated Circuits (NASIC) fabric that can determine probabilities of output faults taking into account the defect scenarios, the logic and circuit style of the fabric as well as structural redundancy schemes that may be incorporated in the circuits. Evaluation of fault rates using the analytical model for single NASIC tiles show an inequality of the probability of output faulty ‘1’s and ‘0’s. To mitigate the effects of the unequal fault rates, biased voting schemes are introduced and are shown to achieve up to 27% improvement in the reliability of output signals compared to conventional majority voting schemes. NASIC circuits have to be cascaded in order to build larger systems. Furthermore, modular redundancy alone will be insufficient to tolerate high defect rates since multiple input modules may be faulty. Hence incorporation of structural redundancy is crucial. Thus in this thesis, we study the propagation of faults through a cascade of NASIC circuits employing the conventional structural redundancy scheme which is referred to here as the Regular Structural Redundancy. In our analysis we find that although circuits with Regular Structural Redundancy achieve greater signal reliability compared to non-redundant circuits, the signal reliability rapidly drops along the cascade due to an escalation of faulty ‘0’s. This effect is attributed to the poor tolerance of input faulty ‘0’s exhibited by circuits with the Regular Structural Redundancy. Having identified this, we design a new scheme called the Staggered Structural Redundancy prioritizing the tolerance of input faulty ‘0’s. A cascade of circuits employing the Staggered Structural Redundancy is shown to maintain signal reliability greater than 0.98 for over 100 levels of cascade at 5% defect rate whereas the signal reliability for a cascade of circuits with the Regular Structural Redundancy dropped to 0.5 after 7 levels of cascade.

<p>With the Moore’s Law era coming to an end, much attention has been given to novel nanoelectronic devices as a key driving force behind technological innovation. Utilizing the inherent device physics of nanoelectronic components, for sensory and computational tasks have proven to be useful in reducing the area and energy requirements of the underlying hardware fabrics. In this work we demonstrate how the intrinsic noise present in nano magnetic devices can pave the pathway for energy efficient neuromorphic hardware. Furthermore, we illustrate how the unique magnetic properties of such devices can be leveraged for accurate estimation of environmental magnetic fields. We focus on spintronic technologies in particular, due to the low current and energy requirements in contrast to traditional CMOS technologies.</p><p>Image segmentation is a crucial pre-processing stage used in many object identification tasks that involves simplifying the representation of an image so it can be conveniently analyzed in the later stages of a problem. This is achieved through partitioning a complicated image into specific groups based on color, intensity or texture of the pixels of that image. Locally Excitatory Globally Inhibitory Oscillator Network or LEGION is one such segmentation algorithm, where synchronization and desynchronization between coupled oscillators are used for segmenting an image. In this work we present an energy efficient and scalable hardware implementation of LEGION using stochastic Magnetic Tunnel Junctions that leverage the fast parallel</p><p> nature of the algorithm. We demonstrate that the proposed hardware is capable of segmenting binary and gray-scale images with multiple objects more efficiently than<br> existing hardware implementations. </p><p>It is understood that the underlying device physics of spin devices can be used for emulating the functionality of a spiking neuron. Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway of achieving brain-like compact and energy-efficient cognitive intelligence. Current computational models attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning and inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. Our work attempts to explore the design space and analyze the performance of nanomagnet based stochastic neuromorphic computing architectures, for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets as they are scaled into the superparamagnetic regime.<br></p><p>Next we investigate how the magnetic properties of spin devices can be utilized for real world sensory applications. Magnetic Tunnel Junctions can efficiently translate variations in external magnetic fields into variations in electrical resistance. We couple this property of Magnetic Tunnel Junctions with Amperes law to design a non-invasive sensor to measure the current flowing through a wire. We demonstrate how undesirable effects of thermal noise and process variations can be suppressed through novel analog and digital signal conditioning techniques to obtain reliable and accurate current measurements. Our results substantiate that the proposed noninvasive current sensor surpass other state-of-the-art technologies in terms of noise and accuracy.<br></p><br>

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<p dir="ltr">Knowledge of transport processes plays a critical role in the development and application of materials in many technologies. As manufacturing technologies continue to push the geometries of materials to smaller scales, traditional means of predicting and measuring transport properties begin to fail. Micro and nanoscopic effects tend to alter transport phenomena in materials, leading to new physics and different properties from the bulk state. In particular, the dynamics of thermal transport of a material varies greatly in both spatial and temporal senses. Different energy carriers have intrinsically different mechanisms of thermal transport; depending on the time and lengths scales in question, the contribution to the overall thermal transport by one carrier may be vastly different than others. To characterize and understand the dynamics of thermal transport at these small scales, novel ultrafast experimental techniques and theories are crucially needed. This work will discuss the efforts made to develop a framework to measure and differentiate the dynamics of transport processes of a material due to different energy carriers using ultrafast optical techniques. This dissertation is organized as follows.</p><p dir="ltr">Chapter 1 gives a background in the theory of thermal transport. This will serve as the foundation for the physical models that are used to extract thermal properties from experimental works. A brief review of the advances in ultrafast experimental and theoretical works will also be given. This will assist in placing this work in the context of ongoing work in the thermal transport community. Chapter 2 illustrates the experimental setups and physical models used to measure the effective thermal transport properties of thin film materials. Steady-state optical measurements are used to quantify the effective, in-plane, anisotropic, thermal conductivity of a 2D material. Time resolved, ultrafast optical measurements are used to quantify the effective, out-of-plane, thermal conductivity of a material. Chapters 3 and 4 demonstrate the capabilities of an ultrafast spatiotemporal scanning pump-probe system, where the high temporal and nanometric resolution measurements directly probe the electron contribution to thermal transport in metals as well as the ambipolar diffusion of carriers in semiconductors. Lastly, Chapter 5 summarizes this dissertation and provides a discussion on the use of the developed experimental capabilities to probe transport of emerging materials.</p>

<p dir="ltr">This work introduces an innovative algorithm for co-simulating time-dependent Drift Diffusion (DD) equations with Maxwell\textquotesingle s equations to characterize semiconductor devices. Traditionally, the DD equations, derived from the Boltzmann transport equations, are used alongside Poisson\textquotesingle s equation to model electronic carriers in semiconductors. While DD equations coupled with Poisson\textquotesingle s equation underpin commercial TCAD software for micron-scale device simulation, they are limited by electrostatic assumptions and fail to capture time dependent high-frequency effects. Maxwell\textquotesingle s equations are fundamental to classical electrodynamics, enabling the prediction of electrical performance across frequency range crucial to advanced device fabrication and design. However, their integration with DD equations has not been studied thoroughly. The proposed method advances current simulation techniques by introducing a new broadband patch-based method to solve time-domain 3-D Maxwell\textquotesingle s equations and integrating it with the solution of DD equations. This technique is free of the low-frequency breakdown issues prevalent in conventional full-wave simulations. Meanwhile, it enables large-scale simulations with reduced computational complexity. This work extends the simulation to encompass the complete device, including metal contacts and interconnects. Thus, it captures the entire electromagnetic behavior, which is especially critical in electrically larger systems and high-frequency scenarios. The electromagnetic interactions of the device with its contacts and interconnects are investigated, providing insights into performance at the chip level. Validation through numerical experiments and comparison with results from commercial TCAD tools confirm the effectiveness of the proposed method. </p>

No / Nanoscale reversed-phase liquid chromatography (LC) combined with electrospray ionization-tandem mass spectrometry (ESI-MS/MS) has been used as a method for the direct identification of multiple cytochrome P450 (P450) isoforms found in male and female rat liver. In this targeted proteomic approach, rat liver microsomes were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by in-gel tryptic digestion of the proteins present in the 48- to 62-kDa bands. The resultant peptides were extracted and analyzed by LC-ESI-MS/MS. P450 identifications were made by searching the MS/MS data against a rat protein database containing 21,576 entries including 47 P450s using Sequest software (Thermo Electron, Hemel Hempstead, UK). Twenty-four P450 isoforms from the subfamilies 1A, 2A, 2B, 2C, 2D, 2E, 3A, 4A, 4F, CYP17, and CYP19 were positively identified in rat liver.

<p>  </p> <p>3D printing techniques have been applied in many fields to provide a potential for complex fabrication, and photopolymerization methods are the current possible path to fabricate nanoscale 3D structures. Multi-photon lithography is the most common tool to reach below 100-nm resolution. These methods require femtosecond lasers to reliably create sophisticated 3D polymeric nanostructures using nonlinear photopolymerization of a light-sensitive resin. Though these methods provide high accuracy and flexibility in advanced fabrication, they are essentially limited by their cost and throughput. Therefore, in this work, multiple approaches were examined to develop new methods for one-photon nonlinear 3D printing. </p> <p>By controlling multiple competing processes in the radical polymerization scheme, a nonlinear photopolymerization effect is achieved using a one-photon absorption process with the assistance of inhibition radicals and controlled diffusion. This work makes use of this nonlinear response to fabricate 2D/3D structures using a continuous-wave diode laser, demonstrating a significantly more cost-efficient source for 3D nanolithography. In addition, a numerical model was constructed with the highly nonlinear response by actively controlling the consumption of the initiators with the assistance of these inhibitors, and it shows the same trend of nonlinearity from experiments. We use this model to study this dosage-based nonlinear response driven by the laser intensity in several 1D and 2D scenarios with different inputs and predicted the polymerization results in a confined voxel in the resin to support the observations from the experiments. Besides the demonstration of current one-photon nonlinear 3D printing, this work also involves some results of nonlinear response by operating local oxygen concentration and a two-step absorption nonlinear photoinitiator. These results help us to further study the potential of increasing the throughput of the one-photon nonlinear 3D printing process. </p> <p>In conclusion, a new one-photon-based dose nonlinear process is introduced in this dissertation to achieve nanoscale 3D printing with a low-cost-405-nm diode laser operating at milliwatt level. By controlling the activation and transport of initiating and inhibiting radicals, we achieve patterning of the nanoscale features at a high scanning speed.</p>

<p dir="ltr">Flash Nanoprecipitation (FNP) is a technique that allows organic nanocarriers (NCs) with core-shell architecture to be prepared reproducibly and at scale. The surface shell may be designed independently of the content in the core. This can allow for encapsulated active ingredients to be delivered to areas of the plant where they naturally would not move to but are needed, the biodistribution becoming a function of NC properties and release of active from the NC. The scalability of FNP is also attractive, since large scale production is ultimately required for commercialization of novel agrochemical solutions. In Chapter 3 scalable NCs encapsulating streptomycin (STP) have been prepared at high encapsulation efficiency (EE) and with controlled release of the antibiotic (< 5%). A surface-similar NC has been shown to translocate (~ 6%) to the roots of citrus trees under controlled conditions after foliar spraying. In vitro efficacy suggests that, if enough NCs containing STP are able to reach the phloem sections of trees where CLas resides at sufficient concentrations under field conditions, then this novel formulation may be able to offer an effective solution for managing the disease. Chapter 4 highlights the challenges in encapsulating weakly hydrophobic fungicides via FNP, the strategies that were employed to module fungicide solubility, and initial quantitative efforts to determine fungicide EE in a reliable and accurate manner. Even without full knowledge about the form in which a particular fungicide, mefentrifluconazole (MFZ), was present in the NCs that were applied to turfgrass during a greenhouse biodistribution test, the novel formulation provided higher MFZ recovery in the lower roots than the conventional treatment 7 days after application. It also presented sustained higher recovery of MFZ on the blades for up to 3 days and after blade clipping at 14 days. These results may indicate that MFZ was present in the vasculature.</p>