In this work we present a novel method for improving the high-temperature performance of silicon photomultipliers (SiPMs) via focused ion beam (FIB) modification of individual microcells. The literature suggests that most of the dark count rate (DCR) in a SiPM is contributed by a small percentage (<5%) of microcells. By using a FIB to electrically deactivate this relatively small number of microcells, we believe we can greatly reduce the overall DCR of the SiPM at the expense of a small reduction in overall photodetection efficiency, thereby improving its high temperature performance. In this report we describe our methods for characterizing the SiPM to determine which individual microcells contribute the most to the DCR, preparing the SiPM for FIB, and modifying the SiPM using the FIB to deactivate the identified microcells.
Time-resolved X-ray thermometry is an enabling technology for measuring temperature and phase change of components. However, current diagnostic methods are limited in their ability due to the invasive nature of probes or the requirement of coatings and optical access to the component. Our proposed developments overcome these challenges by utilizing X-rays to directly measure the objects temperature. Variable-Temperature X-ray Diffraction (VT-XRD) was performed over a wide range of temperatures and diffraction angles and was performed on several materials to analyze the patterns of the bulk materials for sensitivity. "High-speed" VT-XRD was then performed for a single material over a small range of diffraction angles to see how fast the experiments could be performed, whilst still maintaining peaks sufficiently large enough for analysis.
The goal of this project was test how different representations of state uncertainty impact human decision making. Across a series of experiments, we sought to answer fundamental questions about human cognitive biases and how they are impacted by visual and numerical information. The results of these experiments identify problems and pitfalls to avoid when for presenting algorithmic outputs that include state uncertainty to human decision makers. Our findings also point to important areas for future research that will enable system designers to minimize biases in human interpretation for the outputs of artificial intelligence, machine learning, and other advanced analytic systems.
Advanced, superscalar microprocessors ((Formula presented.)) are highly susceptible to wear-out failures because of their highly complex, densely packed circuit structure and extreme operational frequencies. Although many types of fault detection and mitigation strategies have been proposed, none have addressed the specific problem of detecting faults that lead to information leakage events on I/O channels of the (Formula presented.). Information leakage can be defined very generally as any type of output that the executing program did not intend to produce. In this work, we restrict this definition to output that represents a security concern, and in particular, to the leakage of plaintext or encryption keys, and propose a counter-based countermeasure to detect faults that cause this type of leakage event. Fault injection (FI) experiments are carried out on two RISC-V microprocessors emulated as soft cores on a Xilinx multi-processor System-on-chip (MPSoC) FPGA. The (Formula presented.) designs are instrumented with a set of counters that records the number of transitions that occur on internal nodes. The transition counts are collected from all internal nodes under both fault-free and faulty conditions, and are analyzed to determine which counters provide the highest fault coverage and lowest latency for detecting leakage faults. We show that complete coverage of all leakage faults is possible using only a single counter strategically placed within the branch compare logic of the (Formula presented.).
The purpose of our report is to discuss the notion of entropy and its relationship with statistics. Our goal is to provide a manner in which you can think about entropy, its central role within information theory and relationship with statistics. We review various relationships between information theory and statistics—nearly all are well-known but unfortunately are often not recognized. Entropy quantities the "average amount of surprise" in a random variable and lies at the heart of information theory, which studies the transmission, processing, extraction, and utilization of information. For us, data is information. What is the distinction between information theory and statistics? Information theorists work with probability distributions. Instead, statisticians work with samples. In so many words, information theory using samples is the practice of statistics.
Quantifying the sensitivity - how a quantity of interest (QoI) varies with respect to a parameter – and response – the representation of a QoI as a function of a parameter - of a computer model of a parametric dynamical system is an important and challenging problem. Traditional methods fail in this context since sensitive dependence on initial conditions implies that the sensitivity and response of a QoI may be ill-conditioned or not well-defined. If a chaotic model has an ergodic attractor, then ergodic averages of QoIs are well-defined quantities and their sensitivity can be used to characterize model sensitivity. The response theorem gives sufficient conditions such that the local forward sensitivity – the derivative with respect to a given parameter - of an ergodic average of a QoI is well-defined. We describe a method based on ergodic and response theory for computing the sensitivity and response of a given QoI with respect to a given parameter in a chaotic model with an ergodic and hyperbolic attractor. This method does not require computation of ensembles of the model with perturbed parameter values. The method is demonstrated and some of the computations are validated on the Lorenz 63 and Lorenz 96 models.
This project was broadly motivated by the need for new hardware that can process information such as images and sounds right at the point of where the information is sensed (e.g. edge computing). The project was further motivated by recent discoveries by group demonstrating that while certain organic polymer blends can be used to fabricate elements of such hardware, the need to mix ionic and electronic conducting phases imposed limits on performance, dimensional scalability and the degree of fundamental understanding of how such devices operated. As an alternative to blended polymers containing distinct ionic and electronic conducting phases, in this LDRD project we have discovered that a family of mixed valence coordination compounds called Prussian blue analogue (PBAs), with an open framework structure and ability to conduct both ionic and electronic charge, can be used for inkjet-printed flexible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. Retention of programmed states is improved by nearly two orders of magnitude compared to the extensively studied organic polymers, thus enabling in-memory compute and avoiding energy costly off-chip access during training. We demonstrate dopamine detection using PBA synapses and biocompatibility with living neurons, evoking prospective application for brain - computer interfacing. By application of electron transfer theory to in-situ spectroscopic probing of intervalence charge transfer, we elucidate a switching mechanism whereby the degree of mixed valency between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory (DFT) .
This report summarizes the needs, challenges, and opportunities associated with carbon-free energy and energy storage for manufacturing and industrial decarbonization. Energy needs and challenges for different manufacturing and industrial sectors (e.g., cement/steel production, chemicals, materials synthesis) are identified. Key issues for industry include the need for large, continuous on-site capacity (tens to hundreds of megawatts), compatibility with existing infrastructure, cost, and safety. Energy storage technologies that can potentially address these needs, which include electrochemical, thermal, and chemical energy storage, are presented along with key challenges, gaps, and integration issues. Analysis tools to value energy storage technologies in the context of manufacturing and industrial decarbonizations are also presented. Material is drawn from the Energy Storage for Manufacturing and Industrial Decarbonization (Energy StorM) Workshop, held February 8 - 9, 2022. The objective was to identify research opportunities and needs for the U.S. Department of Energy as part of its Energy Storage Grand Challenge program.
This report documents a method for the quantitative identification of radionuclides of potential interest for accident consequence analysis involving advanced nuclear reactors. Based on previous qualitative assessments of radionuclide inventories for advanced reactors coupled with the review of a radiological inventory developed for a heat pipe reactor, a 1 Ci activity airborne release was calculated for 137 radionuclides using the MACCS 4.1 code suite. Several assumptions regarding release conditions were made and discussed herein. The potential release of a heat pipe reactor inventory was also modeled following the same assumptions. Results provide an estimation of the relative EARLY and CHRONC phase dose contribution from advanced reactor radionuclides and are normalized to doses from equivalent releases of I-131 and Cs-137, respectively. Ultimately, a list of 69 radionuclides with EARLY or CHRONC dose contributions at least 1/100th that of I-131 or Cs-137, respectively – 48 of which are currently considered for LWR consequence analyses – was identified of being of potential importance for analyses involving a heat pipe reactor.
Tritium has a unique physical and chemical behavior which causes it to be highly mobile in the environment. As it behaves similarly to hydrogen in the environment, it may also be readily incorporated into the water cycle and other biological processes. These factors and other environmental transformations may also cause the oxidation of an elemental tritium release, resulting in a multiple order of magnitude increase in dose coefficient and radiotoxicity. While source term development and understanding for advanced reactors are still underway, tritium may be a radionuclide of interest. It is thus important to understand how tritium moves through the environment and how the MACCS accident consequence code handles acute tritium releases in an accident scenario. Additionally, existing tritium models may have functionalities that could inform updates to MACCS to handle tritium. In this report tritium transport is reviewed and existing tritium models are summarized in view of potential updates to MACCS.