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Low back pain is a common and potentially debilitating disorder. Pain due to lumbar facet joint pathology is estimated to occur in 15% to 45% of those with low back pain. Radiofrequency ablation (RFA) is a common interventional pain management procedure used to treat lumbar facet joint disease (LFJD). RFA is available for patients when conservative treatment has been unsuccessful. A major benefit to RFA is the diminished need for the use of opioid and other potentially harmful medications. RFA also has the potential to prevent the need for more invasive treatment. The procedures necessary prior to RFA and RFA itself are performed in outpatient settings and allow patients to quickly return to their normal activities post procedure. This article describes the risk factors, pathophysiology, diagnosis, treatment, and nursing considerations of a patient with LFJD. The steps to an RFA and the RFA procedure itself are also discussed.

The IBM zNext's Telum II processor [1] is a full-stack overhaul of its predecessor [2], featuring a modified 5nm Samsung bulk technology [3], new IBM-created standard cell and IP libraries, and a variety of further design-technology co-optimizations (DTCO). While die size grew from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$530\text{mm}^{2}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$600\text{mm}^{2}$</tex> (1.13×), each chip in the dual-chip module became denser, featuring 1.3T shapes (1.25×), 43B transistors (1.38×), and 38km of wire (1.28×) across 18 BEOL layers. As IBM's first 5nm processor, Telum II was a concurrent hierarchical design effort, with libraries, IP blocks (array / regfile / analog / phy / custom), and all gate-level entities designed simultaneously. These data volume and concurrent design pressures drove a variety of abstraction, modeling, and other EDA innovations in addition to those involving power, performance, area, and reliability targets.

Near-eutectic tin-bismuth alloy is a strong contender as the solder of choice for low-temperature electronic assembly applications. This paper will describe the recent significant developments by the iNEMI project team on the understanding of the electromigration behavior of Sn-Bi solder which has a unique physical metallurgy and electromigration behavior quite different for that of Sn-Ag-Cu (SAC) solder and other solders in common use today. The high rate of Bi electromigration and its high homologous temperature sets the near-eutectic Sn-Bi solder apart from the SAC and the Sn-Pb solders. The latter have lower homologous temperatures and have no one element dominating the electromigration phenomenon that can be readily tracked to monitor their electromigration behavior. In Sn-Bi alloys, the rates of Bi electromigration are clearly reflected in the rates of electrical resistance increase making it more convenient to use the fundamental physics-based electromigration equations rather than Black’s somewhat empirical equation that deals with the mean time to failure of solder joints. The high homologous temperature of Sn-Bi alloys makes in-situ study of electromigration in a scanning electron microscope more convenient, resulting in a much deeper understanding of the physical metallurgy and electromigration of the Sn-Bi alloys. For example, recent in-situ studies have shown that the root cause of decreasing solder electrical resistance in the early stage of electromigration, very evident at low temperatures and low current densities, is due to the microstructural coarsening of the alloy. When results are summarized on Arrhenius plots, the prediction of electromigration life under any given field condition becomes straightforward.

Introduction/Purpose: Cerebral venous thrombosis (CVT) is traditionally managed with anticoagulation. However, for patients who fail anticoagulation or with severe symptoms, endovascular thrombectomy (EVT) has emerged as a treatment option. Objective: This study evaluates outcomes, complications, and comorbidities associated with EVT in patients with CVT. Methods: The 2015-2019 National Inpatient Sample (NIS) was queried for patients admitted to hospitals with ICD-10 diagnosis codes for CVT and procedure codes for EVT. Demographics, comorbidities, complications, and discharge dispositions were compared between patients undergoing EVT and medical management. Multivariate logistic regression determined independent predictors of outcomes, controlling for age, gender, cerebral edema/herniation, hemorrhage, mechanical ventilation, Elixhauser Comorbidity Index, and National Institute of Health Stroke Scale (NIHSS) severity. Odds ratios (OR) were calculated for each complication/outcome. All statistical analyses were performed using Statistical Product and Service Solutions (SPSS) Statistical Software Version 29. Results: A total of 36,005 patients with CVT were identified. Of those, 325 (0.9%) underwent EVT. Patients who underwent EVT were older (49.28±19.08 years vs 43.62±23.29 years, p &lt; 0.001), more likely to be female (61.5% vs. 54.8%, p = 0.016), and had higher rates of diabetes mellitus (18.5% vs. 13.5%, p = 0.012), hypertension (47.7% vs. 31.3%, p &lt; 0.001), and obesity (13.8% vs. 8.1%, p &lt; 0.001). They also had higher NIHSS scores (13.15 vs. 6.17, p &lt; 0.001). EVT was associated with higher in-hospital mortality (15.4% vs. 4.6%, OR 1.627, p = 0.007) and lower routine discharge (29.2% vs. 57%, OR 0.599, p &lt; 0.001). Conclusions: EVT patients had more severe conditions and higher mortality, potentially reflecting baseline severity rather than risks of EVT itself. Careful patient selection is crucial to improving outcomes in this population. Further studies are needed to refine patient selection criteria for EVT in CVT.

The rapid emergence of AI models, specifically large language models (LLMs) requiring large amounts of compute, drives the need for dedicated AI inference hardware. During deployment, compute utilization (and thus power consumption) can vary significantly across layers of an AI model, number of tokens, precision, and batch size [1]. Such wide variation, which may occur at fast time scales, poses unique challenges in optimizing performance within the system-level specifications for discrete accelerator cards, including not just average power consumption, but also peak instantaneous current draw, which may require consideration of time constants down to μs-scale [2]. Prior current-limiting systems [2], [3], which use reactive schemes and often target general-purpose processors, may not be sufficient for AI workloads. This work leverages the unique characteristic of AI workloads, which allows predictive compile-time software optimization and proposes a new power management architecture to minimize worst-case margins and realize the potential of AI accelerators. In addition, due to wide variation of power consumption across card components in AI workloads, sensing the card-level (vs. chip-level) current provides more opportunity for optimization. A new software-assisted feed-forward current-limiting scheme is thus proposed in conjunction with PCIe-card-level closed-loop control to maximize performance under sub-ms peak current constraints.

A product-ready L2 cache (L2C) design based on 6T ultra-dense SRAM cells with novel circuits capable of boosting word-line, cell, and, bit-line supplies independently using single supply and metal coupling capacitance is demonstrated for the first time in 5nm technology. A metal short detection circuit is provided to increase the robustness of the design. Hardware data shows that L2C operates with a minimum supply of 0.57V and reaches a maximum operating frequency of 1.9GHz at 1.1V.

Methods for evaluation of junction in power semiconductor devices are discussed. An Insulated Gate Bipolar Transistor (IGBT) chip was selected for the experiment. Samples were alternatively mechanically polished, cleaved, or in a third case, sectioned with an experimental FIB (Focused Ion Beam) section. From here, sensitive Electron Beam Induced Current (EBIC) measurements were undertaken and results were compared. There was little difference in the ability to detect depletion zones between the techniques, indicating that Ga+ contamination or amorphization from the FIB did not prevent the collection of useful data from the p/n junctions. During the course of experimentation, it was determined that the pixel dwell time during electron beam stimulation had a strong effect on removing artifacts and increasing the contrast of the result. In the optimized analysis, the EBIC work crisply imaged the depletion zones of not only the P body / N- drift regions, but also the N+ / body regions in the same image. Additional observations were made to compare the different physical extents of the implant regions by Passive Voltage Contrast (PVC) versus the depletion zones from the EBIC results.

A method for extracting anisotropic permittivity of PCB substrate is proposed and tested on a PCB test vehicle by using a rectangular stripline resonator loaded with a via array. As a value-added measurement after traditional isotropic permittivity measurement methods like SPP, for the first time, this method can add PCB substrate anisotropic permittivity of a practical industrial application for electrical modeling.