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At least 559 records · Page 31

Introduction to Photovoltaics Failure Analysis and Reliability.

Photovoltaics (PV) are becoming an increasingly important solution to meet the demand for renewable energy. The rapid growth and adoption of this technology means that microelectronics failure analysis and reliability experts may be called upon to address current and future challenges. The article here provides a brief primer on common failure mechanisms found in PV component devices, as well as the associated failure analysis techniques to identify them.

14 SOLAR ENERGY↗

Probing low noise at the MOS interface with a spin-orbit qubit.

The silicon metal-oxide-semiconductor (MOS) material system is technologically important for the implementation of electron spin-based quantum information technologies. Researchers predict the need for an integrated platform in order to implement useful computation, and decades of advancements in silicon microelectronics fabrication lends itself to this challenge. However, fundamental concerns have been raised about the MOS interface (e.g. trap noise, variations in electron g-factor and practical implementation of multi-QDs). Furthermore, two-axis control of silicon qubits has, to date, required the integration of non-ideal components (e.g. microwave strip-lines, micro-magnets, triple quantum dots, or introduction of donor atoms). In this paper, we introduce a spin-orbit (SO) driven singlet- triplet (ST) qubit in silicon, demonstrating all-electrical two-axis control that requires no additional integrated elements and exhibits charge noise properties equivalent to other more model, but less commercially mature, semiconductor systems. We demonstrate the ability to tune an intrinsic spin-orbit interface effect, which is consistent with Rashba and Dresselhaus contributions that are remarkably strong for a low spin-orbit material such as silicon. The qubit maintains the advantages of using isotopically enriched silicon for producing a quiet magnetic environment, measuring spin dephasing times of 1.6 μs using 99.95% 28 Si epitaxy for the qubit, comparable to results from other isotopically enhanced silicon ST qubit systems. This work, therefore, demonstrates that the interface inherently provides properties for two-axis control, and the technologically important MOS interface does not add additional detrimental qubit noise. isotopically enhanced silicon ST qubit systems

Jock, Ryan Michael↗

Zeolite nanosheet membrane

Oxide materials, thin films, coatings, and methods of preparing the same are disclosed herein. In certain embodiments the oxide material can have an MWW type framework or an MFI type framework. In one embodiment, the method includes: providing a suspension of an exfoliated layered oxide material in a solvent; and filtering the suspension through a porous support to provide a film of the oxide material, optionally directly on the porous support. Secondary grown films of the oxide material and methods of preparing the same are also provided. Thin zeolite films are attractive for a wide range of applications including molecular sieve membranes and catalytic membrane reactors, permeation barriers, low dielectric constant materials for microelectronics and sensor components for selective sensing.

Tsapatsis, Michael↗

Nanocomposite Coatings with Tunable Properties Prepared by Atomic Layer Deposition

We report that nanocomposite coatings, comprised of a precisely blended mixture of components, have diverse applications in microelectronics, optics, sensors, and solid-state detectors. By varying the composition of the mixture, the electrical, optical, and physical properties of the coatings can be “tuned” over almost the full range of the individual components, and can sometimes yield properties distinct from those of any of the constituents. There are many techniques for producing nanocomposite coatings, but atomic layer deposition (ALD) is particularly well suited for controlling the properties of the fabricated coatings. ALD uses alternating cycles of gaseous precursors to a solid surface to deposit materials in an atomic layer-by-layer fashion. Because a chemical reaction on the surface naturally terminates when the surface functional groups are all reacted, the amount of material deposited in one ALD cycle is easily controlled. This self-limiting property, coupled with facile diffusion of the precursor vapors into narrow pores and voids, allows complex, 3-dimensional substrates to be coated with excellent uniformity and conformality. ALD can be used to deposit a wide variety of materials including metal oxides, nitrides, sulfides, and even pure elements. By alternating between two materials (e.g., a metal and a metal oxide), we can synthesize complex nanocomposite coatings. Lastly, the thickness of the coating is controlled by the total number of ALD cycles performed, and the composition is controlled by the ratio of the ALD cycles executed for each of the two components.

36 MATERIALS SCIENCE↗

Thermally managed LED arrays assembled by printing

Provided herein are electronic devices including arrays of printable light emitting diodes (LEDs) having device geometries and dimensions providing enhanced thermal management and control relative to conventional LED-based lighting systems. The systems and methods described provide large area, transparent, and/or flexible LED arrays useful for a range of applications in microelectronics, including display and lightning technology. Methods are also provided for assembling and using electronic devices including thermally managed arrays of printable light emitting diodes (LEDs).

Rogers, John A.↗

Depositing bulk or micro-scale electrodes

Thicker electrodes are provided on microelectronic device using thermo-compression bonding. A thin-film electrical conducting layer forms electrical conduits and bulk depositing provides an electrode layer on the thin-film electrical conducting layer. An insulating polymer layer encapsulates the electrically thin-film electrical conducting layer and the electrode layer. Some of the insulating layer is removed to expose the electrode layer.

36 MATERIALS SCIENCE↗

Ion-implanted thermal barrier

Ion implantation can be used to define a thermal dissipation path that allows for better thermal isolation between devices in close proximity on a microelectronics chip, thus providing a means for higher device density combined with better performance.

42 ENGINEERING↗

Examination of Solvent- Room Temperature Ionic Liquid Interactions using 2D NMR and Computational Chemistry

Rare earth metals (REMs) are the key functioning material found in microelectronics, advanced batteries, and high-efficiency electrical motors. Current methods for producing REMs require high temperatures (i.e. energy intensive) and produce an abundance of hazardous fluoride waste. Room temperature ionic liquids (RTILs) are an ideal alternative electrolyte because they allow for a low-temperature, energy-efficient electrochemical process. RTIL properties such as viscosity and conductivity determine how effective they can be in this process. Since viscosity is directly related to both intra and intermolecular forces, this study sets out to investigate two RTILs-Trihexyltetradecylphosphonium dicyanamide (Cyphos 105) and Trihexyltetradecylphosphonium bistrifimide (Cyphos 109) and their interactions. Results from 2-dimensional nuclear magnetic resonance (2D NMR) spectroscopy show that there is an interaction between the tetradecyl tail and the phosphorus atom of the Cyphos RTIL. In order to establish whether this is an intra or an inter-molecular interaction, DFT-based computational calculations were performed to determine the optimized geometry. Calculations were done both in the gas phase and with propylene carbonate as an augmenting solvent. Preliminary results show that there is minimal intramolecular interaction between the tetradecyl tail and the phosphorus atom of the RTILs, and that the interaction seen in the 2D NMR experiment is likely of an intermolecular nature instead.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for near-sensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Ultramicro to mesopore frameworks for selective separation and storage of noble gases

Methods and materials for the selective capture and storage of preselected materials from gas streams using metal organic framework (MOF) materials are described. In various embodiments preselected target material gases could include noble gasses such as Kr, Xe, Rn, Arultramicro to mesopore frameworks for selective separation and storage of noble gases, other gasses such as I2 or other particular isotopes either naturally occurring or man-made, or another preselected gas capture material such as a target material for legal, regulatory or treaty compliance, or a preselected material from a particular process such as a cleaning or etching agent from semiconducting or microelectronic manufacture, or a portion of an anesthetic gas such as nitrous oxide, isoflurane, sevoflurane or a fluorinated ethers.

Thallapally, Praveen K.↗

Snowmass Computational Frontier: Topical Group Report on Quantum Computing

Quantum computing will play a pivotal role in the High Energy Physics (HEP) science program over the early parts of the 21$^{st}$ Century, both as a major expansion of our capabilities across the Computational Frontier, and in synthesis with quantum sensing and quantum networks. This report outlines how Quantum Information Science (QIS) and HEP are deeply intertwined endeavors that benefit enormously from a strong engagement together. Quantum computers do not represent a detour for HEP, rather they are set to become an integral part of our discovery toolkit. Problems ranging from simulating quantum field theories, to fully leveraging the most sensitive sensor suites for new particle searches, and even data analysis will run into limiting bottlenecks if constrained to our current computing paradigms. Easy access to quantum computers is needed to build a deeper understanding of these opportunities. In turn, HEP brings crucial expertise to the national quantum ecosystem in quantum domain knowledge, superconducting technology, cryogenic and fast microelectronics, and massive-scale project management. The role of quantum technologies across the entire economy is expected to grow rapidly over the next decade, so it is important to establish the role of HEP in the efforts surrounding QIS. Fully delivering on the promise of quantum technologies in the HEP science program requires robust support. It is important to both invest in the co-design opportunities afforded by the broader quantum computing ecosystem and leverage HEP strengths with the goal of designing quantum computers tailored to HEP science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Radioimaging for real-time tracking of high-voltage breakdown

An interferometric radioimager provides real-time, high-fidelity radioimaging of high voltage breakdown (HVB) both internal and external to electrical components at sub-nanosecond and sub-millimeter resolution and has an ability to resolve multiple/spatially-extensive HVB simultaneously. Therefore, radioimaging can be used to screen for early life weakness/failure and enable non-destructive screening of defective electrical components. In particular, radioimaging can detect precursors to catastrophic HVB, allowing for early detection of weakness in critical electrical components. Radioimaging can also be used to track HVB and pinpoint defects in electrical components real time, including transformers, capacitors, cables, switches, and microelectronics.

Tilles, Julia Napolin↗

Advanced Power Electronics and Electric Machines

The advanced power electronics and electric machines (APEEM) research group at the National Renewable Energy Laboratory (NREL) has developed world-class experimental and modeling capabilities for designing and evaluating efficient and reliable power electronics and electric machines thermal management systems. They also design, fabricate and characterize advanced power electronics packaging, and are developing state-of-health monitoring techniques. These researchers deliver safe, reliable, high performing, power-dense components that allow seamless integration between renewable energy sources, electric transportation, and the grid, helping to make widespread electric vehicle (EV) adoption and greenhouse gas emissions reduction more feasible. This document outlines the group's major capabilities in the areas of power electronics; module development and characterization; thermal modeling and management; thermomechanical reliability analysis of devices, modules, inverters/converters, and electric machines; physics-of-failure-based reliability analysis; and microelectronics. It also overviews the group's state-of-the-art equipment for fluid-based thermal management; thermal measurement & characterization; thermomechanical reliability analysis; micro- and power electronics measurement & characterization; and prototype fabrication, as well as the group's world-class modeling and simulation capabilities.

advanced gate drivers↗

The Rise of Intelligent Materials Science: Unleashing the Power of Machine Intelligence in Characterization

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

autonomous↗

Beyond the Hype: Navigating the Promise and Pitfalls of Multi-Modal Models for Materials Science

Multi-modal models offer great potential for accelerating discovery in materials and chemical systems, but their adoption raises crucial questions: What materials science challenges are best addressed by multi-modal approaches? How do we weigh the benefits against the resource investment required for multi-modal data acquisition? And critically, how can we optimize experimental workflows to leverage these models effectively? In this presentation, I will delve into the development of multi-modal characterization and analytics, focusing on their application in the demanding fields of next-generation microelectronics and energy storage materials. I will share challenges encountered in designing these workflows, highlighting lessons learned and posing questions that remain unanswered.

AI↗

Beyond Human Vision: Exploring Materials with Machine Intelligence

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

artificial intelligence↗

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML) and silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

Gonski, Julia [SLAC]↗

Graphical User Interface for a Biasing Board for the PSEC6

The PSEC6 is an application-specific integrated circuit (ASIC) designed for a readout system for a large area picosecond photodetector (LAPPD). The PSEC6 is currently in fabrication and pending testing. The testing system for the PSEC5, the previous iteration of the ASIC, required expensive and non-portable equipment, because the ASIC needs twelve adjustable reference voltages. The new testing system consists of an low-cost, open-source, cross-platform graphical user interface (GUI), a digital system, and a biasing board. The digital system is the interface between the GUI and biasing board, and can be implemented on a microcontroller or field-programmable gate array (FPGA). The biasing board contains twelve digital-to-analog converters (DACs) that are configurable via the GUI, which gives users the ability to write voltage values to all or specific DACs. The GUI was developed in C on Linux using the widget library GTK4 and cross-compiled for Windows compatibility. I2C and SPI protocols were implemented on an Adafruit Feather ESP32-S3 microcontroller to write commands to the DACs and PSEC6. A hardware implementation of the I2C protocol is in development on an FPGA. Since LAPPDs will be used by the Accelerator Neutrino Neutron Interaction Experiment (ANNIE) at Fermilab, the PSEC6 testing system in this internship project can potentially benefit future neutrino research. The project is relevant to the Department of Energy’s microelectronics mission, because the PSEC6 is an ASIC that will handle fast time signals arriving from the detector for readout. It also provided experience with building a cross-platform user interface, practicing digital design and implementation in hardware description language (HDL), and using simulations to inform new design iterations.

Guerrero, Sasha Camila [North Central Coll.]↗