Micron (United States)

ABSTRACT Adoption of additively manufactured electronics for low and high‐volume manufacturing is hindered by available inks and printing variations. Achieving repeatable and consistent features on flexible substrates remains challenging due to ink formulation and process limitations. Herein, we report a ternary‐solvent formulation of 2D graphene‐based ink, optimized to control volatility, surface tension, and viscosity for stable ultrasonic atomization. By optimizing process parameters such as nozzle size, focusing ratio, and platen temperature, we demonstrate high‐resolution printing using an aerosol jet printer (AJP), achieving feature sizes down to 5 µm in the as‐printed state, where linewidths are defined directly by the printing process without post‐print geometric refinement. Thermal annealing is subsequently employed to enhance conductivity. To demonstrate AJP's process flexibility for fabricating functional devices, interdigitated electrodes (IDEs) for capacitive touch sensing and serpentine structures for Joule heating were printed. The printed IDEs operate without dielectric coatings and exhibited consistent capacitive response (∼2 pF) to direct touch stimuli, while the heater demonstrated uniform and tunable thermal output (up to ∼56°C) under low‐power operation (150 mW). These results demonstrate an ink formulation that enables high‐resolution patterning and highlights AJP's potential as a scalable, lithography‐free platform utilizing solution‐processed 2D materials for electronics and sensors.

The increasing prevalence of advanced technology in sports has empowered athletes, coaches, and team management to elevate their performance and fine-tune team tactics. Notably, player collaborations have emerged as a critical factor in reshaping the dynamics of matches, yet a lack of tools for accurate partnership analysis often leads to instinct-driven decisions rather than data-based insights. The aim of this study is to investigate the potential of harnessing machine learning algorithms to evaluate the significance of player partnerships in football and their potential impact on match out-comes. This study innovatively forecasts player partnership formations in live matches, demonstrating adept management of extensive, unstructured datasets to create a robust solution. It offers valuable insights for coaches to predict and build effective player partnerships, resulting in sustainable advantages for the team. The research utilizes machine learning techniques, including Logistic Regression (LR), Decision Tree (DT), K-nearest Neighbor (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), and Random Forest (RF) classifiers. These models were applied and evaluated to construct models and assess player partnerships. Necessary data was sourced from the Kaggle dataset, which encompasses information on 167,798 player assists from 9,074 European league football matches spanning six seasons across five leagues. Machine learning models underwent training and evaluation through an 80 − 20 data split and further verified through cross-validation to ensure robustness across league datasets. Balanced sampling and k-fold cross-validation were used to mitigate data imbalance and validate model generalization. Notably, the SVM, DT and RF models demonstrated impressive accuracy of 99%, with the highest F1-score of 0.99, encompassing all football leagues. The SVM and RF models also achieved the highest precision, at 0.99, albeit with a relatively longer computation time of up to 60 s. Model performance varied marginally across leagues (± 0.3% standard deviation in accuracy and F1-score), indicating stable predictive behaviour across gameplay variations. Unlike prior studies that analyzed team synchronization or motion dynamics, this work uniquely models player partnerships based on assist-goal frequency patterns and forecasts their continuity across matches using machine learning classifiers. This study provides valuable insights for devising strategies that can confer a competitive edge in matches. The findings offer a practical means for team managers and coaches to make well-informed decisions regarding player partnerships.

Abstract X‐ray reflectometry (XRR) can be used as a nondestructive method for thin film thickness measurement. In this study, a multilayer dielectric SiO/SiN (ONON) film stack for 3D NAND is characterized using both transmission electron microscopy (TEM) and XRR. Direct comparison between these two techniques allows us to validate XRR as a process line monitoring tool for quick feedback of process quality of ONON layer structure. The advantages and limitations of these two techniques for ONON layer characterization are discussed, along with insights into data interpretation that support future implementation of XRR in manufacturing environments.

Covalently linked squaraine dimers exhibit characteristic collective excitonic effects based on their packing geometries, which can be controlled by a bridge linking two monomers. A previous study showed that the bridge moiety strongly impacted the excitonic line broadening. Here, we use ultrabroadband two-dimensional electronic spectroscopy to quantify the homogeneous and inhomogeneous line-broadening components. Both rigid and flexible bridges reduce homogeneous broadening relative to the monomer, but the flexible bridge increased inhomogeneous broadening and thereby masked the exchange-narrowing effect. These results reveal that one can design a bridge structure of covalently linked dimers to control both line broadening and packing geometry. • Proposed uses of molecular aggregates are light harvesting & quantum information. • Squaraine dimers exhibit desirable collective excitonic effects. • Rigid (flexible) bridge leads to homogeneous (disordered) sample. • 2D spectroscopy can reveal exchange narrowing even in disordered samples. • All dimers exhibit exchange narrowing, even in presence of disorder.

A mold investigation is a focused, science-driven effort to uncover how contamination gets in, spreads, and survives-no assumptions, no shortcuts. It starts with a thorough analysis of mold recoveries across environmental, process, and product-related factors. Isolates are classified by fungal division, with reproductive state, source, and growth behaviour assessed.From there, environmental monitoring data and product failures are examined to complete the full picture. Facility conditions-structural integrity, airflow, material flow, and sanitation-are evaluated to identify weak spots mold might exploit. Operational procedures and personnel practices are scrutinized, revealing possible gaps that can fuel contamination.By combining these elements, the investigation doesn't just react to mold-it anticipates it. This approach lays the groundwork for targeted, effective controls that protect both product integrity and environmental quality in regulated spaces.

Numerous viral genes are involved in the assembly of herpes simplex virus-1 (HSV-1), but their relative importance and function remain poorly characterised. Transmission electron microscopy has been used to study viral protein function in cells infected with HSV-1 mutants; however, these studies were usually conducted without correlative light microscopy to identify specific viral components. In this study, fluorescent capsid (eYFP-VP26) and envelope (gM-mCherry) proteins were imaged by structured illumination microscopy under cryogenic conditions (cryoSIM) and cellular ultrastructure was captured from the same infected cells using cryo-soft-X-ray tomography (cryoSXT). Nine fluorescent HSV-1 mutants, each lacking a different viral protein, were compared to assess the importance of viral proteins in different stages of HSV-1 morphogenesis. The relative importance of five viral proteins to nuclear egress were ranked (pUL34 >pUL21>VP16>pUL16>pUS3) according to the levels of attenuation observed for each virus. Correlative imaging also revealed the roles of five viral proteins in cytoplasmic envelopment. VP16 was found to be important in capsid delivery to envelopment compartments, while cytoplasmic clusters of virus particles plus features of stalled envelopment not previously described were observed in the absence of pUL11, pUL51, gK, and gE. Finally, this 3D imaging approach was used to capture different assembly stages during cytoplasmic envelopment and to determine that envelopment occurs by particle budding rather than wrapping. The findings demonstrate that tomographic 3D correlative imaging is an emerging technology that sheds new light on viral protein functions and virion morphogenesis.

Abstract In advanced DRAM and NAND structures, the feature size at the targeted cut face during the Focused Ion Beam (FIB) process has been reduced to sub-ten nanometer scale. Even with high-quality SEM resolution level scanned cut-face image data, determining the stopping point of the process remains a significant challenge. To address this, Micron has successfully implemented cut-face probability calculation and sliced image data interpolation methods using machine vision image artificial intelligence platforms (AIPs by Thermo Fisher Scientific) for the Micron’s latest memory devices analysis. This innovation enables machines to render a device’s three-dimensional geometry, infer and predict the upcoming cut face in SEM data points, and determine the stopping point in the FIB thinning process using detector’s raw data signal extractions. This approach avoids signal post-processing and transformation into visible light range for humans, thereby preventing meaningful data loss. The new methodology surpasses human eye detection limits, maximizes SEM resolution utilization, and converts an automation system into an autonomous system capable of deciding where to stop the FIB milling at each cut-face, based on the sensory input of Micron wafer’s cut-face analysis and learning algorithms. This technology has been integrated into its associated FIB platform, augmented by Open-AutoTEM, (iFast Extensibility). Micron defines this autonomous FIB thinning as active auto thinning (AAT), distinguishing it from the conventional fiducial-based FIB auto thinning, known as passive auto thinning (PAT).

Low Power Double Data Rate 5 (LPDDR5) memory is increasingly critical in modern computing systems, particularly in data center storage, mobile and embedded applications where performance and power efficiency are paramount. This paper explores various strategies for optimizing the performance of LPDDR5 subsystems. The focus is on architectural enhancements, power management techniques, and data access optimization. We examine the impact of advanced memory controller algorithms, such as column-to-column delay, activate command queue usage, bank group rotation policy, write clock always on-off usage, delay between 2 read commands for same chip select, delay between 2 write commands for same chip select, adaptive refresh rates and dynamic frequency scaling, on system throughput and latency. Our simulations and emulation run on SoC and silicon board show that these optimization techniques can increase LPDDR5 subsystem efficiency, improving system performance with low power usage by up to 61% in simulation and emulation, and by 13% on silicon. This paper offers crucial insights for designing advanced data center storage, mobile, and embedded systems, enabling more efficient computing solutions.

Radiation exposure from radiological or nuclear events, medical treatments, or spaceflight poses significant health risks, yet human-specific models to investigate radiation effects on skin remain limited. This study establishes a novel in vitro platform using a full-thickness bioengineered human skin equivalent colonized with natural mixed human microbiota (coHSEs) to assess radiation-induced biological responses. We exposed coHSEs to acute doses of up to 4 Gy with x-rays and evaluated their viability, structural integrity, and molecular responses over 25 days. The coHSE model demonstrated sustained viability without dose-dependent opportunistic microbial overgrowth when procedural optimizations were applied. Radiation-induced epidermal remodeling did not compromise tissue architecture or swabbing-based sample collection. Cell proliferation analyses revealed dose- and time-dependent dynamics, with consistent dermal cell density maintained across radiation doses. Comparative multi-omic analyses, including untargeted metabolomics, targeted lipidomics, and 16 S metagenomics, revealed conserved metabolic and microbial responses to radiation in both coHSEs and skin from irradiated mice. Enriched pathways such as arachidonic acid and fatty acid metabolism, along with shifts in microbial taxa including Lachnospiraceae, support the translational relevance of the coHSE model. This system offers a scalable, ethical, and physiologically relevant platform for radiation biology, biodosimetry, and therapeutic development, advancing terrestrial health research with promising application for space research.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-17153-4.