Progress in Nanoporous Templates: Beyond Anodic Aluminum Oxide and Towards Functional Complex Materials

Successful synthesis of ordered porous, multi-component complex materials requires a series of coordinated processes, typically including fabrication of a master template, deposition of materials within the pores to form a negative structure, and a third deposition or etching process to create the final, functional template. Translating the utility and the simplicity of the ordered nanoporous geometry of binary oxide templates to those comprising complex functional oxides used in energy, electronic, and biology applications has been met with numerous critical challenges. This review surveys the current state of commonly used complex material nanoporous template synthesis techniques derived from the base anodic aluminum oxide (AAO) geometry

RESISTIVE SWITCHING CHARACTERISTICS OF NANOSTRUCTURED AND SOLUTION-PROCESSED COMPLEX OXIDE ASSEMBLIES

Miniaturization of conventional nonvolatile (NVM) memory devices is rapidly approaching the physical limitations of the constituent materials. An emerging random access memory (RAM), nanoscale resistive RAM (RRAM), has the potential to replace conventional nonvolatile memory and could foster novel type of computing due to its fast switching speed, high scalability, and low power consumption. RRAM, or memristors, represent a class of two terminal devices comprising an insulating layer, such as a metal oxide, sandwiched between two terminal electrodes that exhibits two or more distinct resistance states that depend on the history of the applied bias. While the sudden resistance reduction into a conductive state in metal oxide insulators has been known for almost 50 years, the fundamental resistive switching mechanism is a complex phenomenon that is still long-debated, complex process. Further improvements to existing memristor performance require a complete understanding of memristive properties under various operation conditions. Additional technical issues also remain, such as the development of facile, low-cost fabrication methods as an alternative to expensive, ultra-high vacuum (UHV) deposition methods. This collection of work explores resistive switching within metal oxide-based memristive material assemblies by analyzing the fundamental physical insulating material properties. Chapter 3 aims to translate the utility and simplicity of the highly ordered anodic aluminum oxide (AAO) template structure to complex, yet more functional (memristive) materials. Functional oxides possessing ordered, scalable nanoporous arrays and nanocapacitor arrays over a large area is of interest to both the fields of next-generation electronics and energy storing/harvesting devices. Here their switching performance will be evaluated using conductive atomic force microscopy (C-AFM). Chapter 4 demonstrates a convective self-assembly fabrication method that effectively enables the synthesis of a low-cost solution processed memristor comprising binary oxide and perovskite ABO3 nanocrystals of varying diameter. Chapter 5 systematically compares the influence of inter-nanoparticle distance on the threshold switching SET voltage of hafnium oxide (HfO2) memristors. Utilizing shorter phosphonic acid ligands with higher binding affinity on the nanocrystal surface enabled a record-low SET voltage to be achieved. Chapter 6 extends the scope to the fine tuning of solution processed memristors with two types of perovskites nanocrystals. The primary advantage of nanocrystal memristors is the ability to draw from additional degrees of freedom by tuning the constituent nanocrystal material properties. Recent advancement of solution phase techniques enables a high degree of controllability over the nanocrystal size and structure. Thus, this work found in this dissertation aims to understand and decouple the effects of the geometric size and substitutional nanocrystal parameters on resistive switching

Multi-task zipping via layer-wise neuron sharing

Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks on-device. Yet the complexity of these neural networks needs to be trimmed down both within-model and cross-model to fit in mobile storage and memory. Previous studies focus on squeezing the redundancy within a single neural network. In this work, we aim to reduce the redundancy across multiple models. We propose Multi-Task Zipping (MTZ), a framework to automatically merge correlated, pre-trained deep neural networks for cross-model compression. Central in MTZ is a layer-wise neuron sharing and incoming weight updating scheme that induces a minimal change in the error function. MTZ inherits information from each model and demands light retraining to re-boost the accuracy of individual tasks. Evaluations show that MTZ is able to fully merge the hidden layers of two VGG-16 networks with a 3.18% increase in the test error averaged on ImageNet and CelebA, or share 39.61% parameters between the two networks with <0.5% increase in the test errors for both tasks. The number of iterations to retrain the combined network is at least 17.8 times lower than that of training a single VGG-16 network. Moreover, experiments show that MTZ is also able to effectively merge multiple residual networks.Comment: Published as a conference paper at NeurIPS 201

W-Air: Enabling personal air pollution monitoring on wearables

Accurate, portable and personal air pollution sensing devices enable quantification of individual exposure to air pollution, personalized health advice and assistance applications. Wearables are promising (e.g., on wristbands, attached to belts or backpacks) to integrate commercial off-the-shelf gas sensors for personal air pollution sensing. Yet previous research lacks comprehensive investigations on the accuracies of air pollution sensing on wearables. In response, we proposed W-Air, an accurate personal multi-pollutant monitoring platform for wearables. We discovered that human emissions introduce non-linear interference when low-cost gas sensors are integrated into wearables, which is overlooked in existing studies. W-Air adopts a sensor-fusion calibration scheme to recover high-fidelity ambient pollutant concentrations from the human interference. It also leverages a neural network with shared hidden layers to boost calibration parameter training with fewer measurements and utilizes semi-supervised regression for calibration parameter updating with little user intervention. We prototyped W-Air on a wristband with low-cost gas sensors. Evaluations demonstrated that W-Air reports accurate measurements both with and without human interference and is able to automatically learn and adapt to new environments.</jats:p