In the nano-regime many materials exhibit properties that are quite different from their bulk counterparts. These nano-properties have been shown to be useful in a wide range of applications with nanomaterials being used for catalysts, in energy production, as protective coatings, and in medical treatment. While there is no shortage of exciting and novel applications, the world of nanomaterials suffers from a lack of large scale manufacturing techniques. The current methods and equipment used for manufacturing nanomaterials are generally slow, expensive, potentially dangerous, and material specific. The research and widespread use of nanomaterials has undoubtedly been hindered by this lack of appropriate tooling. This work details the effort to create a novel nanomaterial synthesis and deposition platform capable of operating at industrial level rates and reliability.

The tool, referred to as Deppy, deposits material via hypersonic impaction, a two chamber process that takes advantage of compressible fluids operating in the choked flow regime to accelerate particles to up several thousand meters per second before they impact and stick to the substrate. This allows for the energetic separation of the synthesis and deposition processes while still behaving as a continuous flow reactor giving Deppy the unique ability to independently control the particle properties and the deposited film properties. While the ultimate goal is to design a tool capable of producing a broad range of nanomaterial films, this work will showcase Deppy's ability to produce silicon nano-particle films as a proof of concept.

By adjusting parameters in the upstream chamber the particle composition was varied from completely amorphous to highly crystalline as confirmed by Raman spectroscopy. By adjusting parameters in the downstream chamber significant variation of the film's density was achieved. Further it was shown that the system is capable of making these adjustments in each chamber without affecting the operation of the other.

The design and development of analog/mixed-signal (AMS) integrated circuits (ICs) is becoming increasingly expensive, complex, and lengthy. Rapid prototyping and emulation of analog ICs will be significant in the design and testing of complex analog systems. A new approach, Programmable ANalog Device Array (PANDA) that maps any AMS design problem to a transistor-level programmable hardware, is proposed. This approach enables fast system level validation and a reduction in post-Silicon bugs, minimizing design risk and cost. The unique features of the approach include 1) transistor-level programmability that emulates each transistor behavior in an analog design, achieving very fine granularity of reconfiguration; 2) programmable switches that are treated as a design component during analog transistor emulating, and optimized with the reconfiguration matrix; 3) compensation of AC performance degradation through boosting the bias current. Based on these principles, a digitally controlled PANDA platform is designed at 45nm node that can map AMS modules across 22nm to 90nm technology nodes. A systematic emulation approach to map any analog transistor to 45nm PANDA cell is proposed, which achieves transistor level matching accuracy of less than 5% for ID and less than 10% for Rout and Gm. Circuit level analog metrics of a voltage-controlled oscillator (VCO) emulated by PANDA, match to those of the original designs in 22nm and 90nm nodes with less than a 5% error. Several other 90nm and 22nm analog blocks are successfully emulated by the 45nm PANDA platform, including a folded-cascode operational amplifier and a sample-and-hold module (S/H). Further capabilities of PANDA are demonstrated by the first full-chip silicon of PANDA which is implemented on 65nm process This system consists of a 24×25 cell array, reconfigurable interconnect and configuration memory. The voltage and current reference circuits, op amps and a VCO with a phase interpolation circuit are emulated by PANDA.

Nonvolatile memory (NVM) technologies have been an integral part of electronic systems for the past 30 years. The ideal non-volatile memory have minimal physical size, energy usage, and cost while having maximal speed, capacity, retention time, and radiation hardness. A promising candidate for next-generation memory is ion-conducting bridging RAM which is referred to as programmable metallization cell (PMC), conductive bridge RAM (CBRAM), or electrochemical metallization memory (ECM), which is likely to surpass flash memory in all the ideal memory characteristics. A comprehensive physics-based model is needed to completely understand PMC operation and assist in design optimization.

To advance the PMC modeling effort, this thesis presents a precise physical model parameterizing materials associated with both ion-rich and ion-poor layers of the PMC's solid electrolyte, so that captures the static electrical behavior of the PMC in both its low-resistance on-state (LRS) and high resistance off-state (HRS). The experimental data is measured from a chalcogenide glass PMC designed and manufactured at ASU. The static on- and off-state resistance of a PMC device composed of a layered (Ag-rich/Ag-poor) Ge30Se70 ChG film is characterized and modeled using three dimensional simulation code written in Silvaco Atlas finite element analysis software. Calibrating the model to experimental data enables the extraction of device parameters such as material bandgaps, workfunctions, density of states, carrier mobilities, dielectric constants, and affinities.

The sensitivity of our modeled PMC to the variation of its prominent achieved material parameters is examined on the HRS and LRS impedance behavior.

The obtained accurate set of material parameters for both Ag-rich and Ag-poor ChG systems and process variation verification on electrical characteristics enables greater fidelity in PMC device simulation, which significantly enhances our ability to understand the underlying physics of ChG-based resistive switching memory.

This work explores how flexible electronics and display technology can be applied to develop new biomedical devices for medical, biological, and life science applications. It demonstrates how new biomedical devices can be manufactured by only modifying or personalizing the upper layers of a conventional thin film transistor (TFT) display process. This personalization was applied first to develop and demonstrate the world's largest flexible digital x-ray detector for medical and industrial imaging, and the world's first flexible ISFET pH biosensor using TFT technology. These new, flexible, digital x-ray detectors are more durable than conventional glass substrate x-ray detectors, and also can conform to the surface of the object being imaged. The new flexible ISFET pH biosensors are >10X less expensive to manufacture than comparable CMOS-based ISFETs and provide a sensing area that is orders of magnitude larger than CMOS-based ISFETs. This allows for easier integration with area intensive chemical and biological recognition material as well as allow for a larger number of unique recognition sites for low cost multiple disease and pathogen detection.

The flexible x-ray detector technology was then extended to demonstrate the viability of a new technique to seamlessly combine multiple smaller flexible x-ray detectors into a single very large, ultimately human sized, composite x-ray detector for new medical imaging applications such as single-exposure, low-dose, full-body digital radiography. Also explored, is a new approach to increase the sensitivity of digital x-ray detectors by selectively disabling rows in the active matrix array that are not part of the imaged region. It was then shown how high-resolution, flexible, organic light-emitting diode display (OLED) technology can be used to selectively stimulate and/or silence small groups of neurons on the cortical surface or within the deep brain as a potential new tool to diagnose and treat, as well as understand, neurological diseases and conditions. This work also explored the viability of a new miniaturized high sensitivity fluorescence measurement-based lab-on-a-chip optical biosensor using OLED display and a-Si:H PiN photodiode active matrix array technology for point-of-care diagnosis of multiple disease or pathogen biomarkers in a low cost disposable configuration.

Several state of the art, monitoring and control systems, such as DC motor

controllers, power line monitoring and protection systems, instrumentation systems and battery monitors require direct digitization of a high voltage input signals. Analog-to-Digital Converters (ADCs) that can digitize high voltage signals require high linearity and low voltage coefficient capacitors. A built in self-calibration and digital-trim algorithm correcting static mismatches in Capacitive Digital-to-Analog Converter (CDAC) used in Successive Approximation Register Analog to Digital Converters (SARADCs) is proposed. The algorithm uses a dynamic error correction (DEC) capacitor to cancel the static errors occurring in each capacitor of the array as the first step upon power-up and eliminates the need for an extra calibration DAC. Self-trimming is performed digitally during normal ADC operation. The algorithm is implemented on a 14-bit high-voltage input range SAR ADC with integrated dynamic error correction capacitors. The IC is fabricated in 0.6-um high voltage compliant CMOS process, accepting up to 24Vpp differential input signal. The proposed approach achieves 73.32 dB Signal to Noise and Distortion Ratio (SNDR) which is an improvement of 12.03 dB after self-calibration at 400 kS/s sampling rate, consuming 90-mW from a +/-15V supply. The calibration circuitry occupies 28% of the capacitor DAC, and consumes less than 15mW during operation. Measurement results shows that this algorithm reduces INL from as high as 7 LSBs down to 1 LSB and it works even in the presence of larger mismatches exceeding 260 LSBs. Similarly, it reduces DNL errors from 10 LSBs down to 1 LSB. The ADC occupies an active area of 9.76 mm2.

There is an ever growing need for larger memories which are reliable and fast. New technologies to implement non-volatile memories which are large, fast, compact and cost-efficient are being studied extensively. One of the most promising technologies being developed is the resistive RAM (ReRAM). In ReRAM the resistance of the device varies with the voltage applied across it. Programmable metallization cells (PMC) is one of the devices belonging to this category of non-volatile memories.

In order to advance the development of these devices, there is a need to develop simulation models which replicate the behavior of these devices in circuits. In this thesis, a verilogA model for the PMC has been developed. The behavior of the model has been tested using DC and transient simulations. Experimental data obtained from testing PMC devices fabricated at Arizona State University have been compared to results obtained from simulation.

A basic memory cell known as the 1T 1R cell built using the PMC has also been simulated and verified. These memory cells have the potential to be building blocks of large scale memories. I believe that the verilogA model developed in this thesis will prove to be a powerful tool for researchers and circuit developers looking to develop non-volatile memories using alternative technologies.

In this thesis, the methods of aluminum electroplating in an ionic liquid for silicon solar cell front side metallization were studied. It focused on replacing the current silver screen printing with an alternative metallization technology using a low-cost Earth-abundant metal for mass production, due to the high cost and limited availability of silver. A conventional aluminum electroplating method was employed for silicon solar cells fabrication on both p-type and n-type substrates. The highest efficiency of 17.9% was achieved in the n-type solar cell with a rear junction, which is comparable to that of the same structure cell with screen printed silver electrodes from industrial production lines. It also showed better spiking resistant performance than the common structure p-type solar cell. Further efforts were put on the development of a novel light-induced plating of aluminum technique. The aluminum was deposited directly on a silicon substrate without the assistance of a conductive seed layer, thus simplified and reduced the process cost. The plated aluminum has good adhesion to the silicon surface with the resistivity as low as 4×10–6 -cm. A new demo tool was designed and set up for the light-induced plating experiment, aiming to utilize this technique in large-size solar cells fabrication and mass production. Besides the metallization methods, a comprehensive sensitivity analysis for the efficiency dispersion in the production of crystalline-Si solar cells was presented based on numerical simulations. Temperature variation in the diffusion furnace was the most significant cause of the efficiency dispersion. It was concluded that a narrow efficiency range of ±0.5% absolute is achievable if the emitter diffusion temperature is confined to a 13˚C window, while other cell parameters vary within their normal windows. Possible methods to minimize temperature variation in emitter diffusion were proposed.

Early detection and treatment of disease is paramount for improving human health and wellness. Micro-scale devices promote new opportunities for the rapid, cost-effective, and accurate identification of altered biological states indicative of disease early-onset; these devices function at a scale more sensitive to numerous biological processes. The application of Micro-Electro-Mechanical Systems (MEMS) in biomedical settings has recently emerged and flourished over course of the last two decades, requiring a deep understanding of material biocompatibility, biosensing sensitively/selectively, biological constraints for artificial tissue/organ replacement, and the regulations in place to ensure device safety. Capitalizing on the inherent physical differences between cancerous and healthy cells, our ultra-thin silicone membrane enables earlier identification of bladder cancer—with a 70% recurrence rate. Building on this breakthrough, we have devised an array to multiplex this sample-analysis in real-time as well as expanding beyond bladder cancer. The introduction of new materials—with novel properties—to augment current and create innovative medical implants requires the careful analysis of material impact on cellular toxicity, mutagenicity, reactivity, and stability. Finally, the achievement of replacing defective biological systems with implanted artificial equivalents that must function within the same biological constraints, have consistent reliability, and ultimately show the promise of improving human health as demonstrated by our hydrogel check valve. The ongoing proliferation, expanding prevalence, and persistent improvement in MEMS devices through greater sensitivity, specificity, and integration with biological processes will undoubtedly bolster medical science with novel MEMS-based diagnostics and therapeutics.

Nanomaterials exhibit unique properties that are substantially different from their bulk counterparts. These unique properties have gained recognition and application for various fields and products including sensors, displays, photovoltaics, and energy storage devices. Aerosol Deposition (AD) is a relatively new method for depositing nanomaterials. AD utilizes a nozzle to accelerate the nanomaterial into a deposition chamber under near-vacuum conditions towards a substrate with which the nanomaterial collides and adheres. Traditional methods for designing nozzles at atmospheric conditions are not well suited for nozzle design for AD methods.

Computational Fluid Dynamics (CFD) software, ANSYS Fluent, is utilized to simulate two-phase flows consisting of a carrier gas (Helium) and silicon nanoparticles. The Cunningham Correction Factor is used to account for non-continuous effects at the relatively low pressures utilized in AD.

The nozzle, referred to herein as a boundary layer compensation (BLC) nozzle, comprises an area-ratio which is larger than traditionally designed nozzles to compensate for the thick boundary layer which forms within the viscosity-affected carrier gas flow. As a result, nanoparticles impact the substrate at velocities up to 300 times faster than the baseline nozzle.

The scaling of transistors has numerous advantages such as increased memory density, less power consumption and better performance; but on the other hand, they also give rise to many reliability issues. One of the major reliability issue is the hot carrier injection and the effect it has on device degradation over time which causes serious circuit malfunctions.

Hot carrier injection has been studied from early 1980's and a lot of research has been done on the various hot carrier injection mechanisms and how the devices get damaged due to this effect. However, most of the existing hot carrier degradation models do not consider the physics involved in the degradation process and they just calculate the change in threshold voltage for different stress voltages and time. Based on this, an analytical expression is formulated that predicts the device lifetime.

This thesis starts by discussing various hot carrier injection mechanisms and the effects it has on the device. Studies have shown charges getting trapped in gate oxide and interface trap generation are two mechanisms for device degradation. How various device parameters get affected due to these traps is discussed here. The physics based models such as lucky hot electron model and substrate current model are presented and gives an idea how the gate current and substrate current can be related to hot carrier injection and density of traps created.

Devices are stressed under various voltages and from the experimental data obtained, the density of trapped charges and interface traps are calculated using mid-gap technique. In this thesis, a simple analytical model based on substrate current is used to calculate the density of trapped charges in oxide and interface traps generated and it is a function of stress voltage and stress time. The model is verified against the data and the TCAD simulations. Finally, the analytical model is incorporated in a Verilog-A model and based on the surface potential method, the threshold voltage shift due to hot carrier stress is calculated.