On critical Fujita exponents for the porous medium equation with a nonlinear boundary condition

AbstractWe establish the critical Fujita exponents for the solution of the porous medium equation ut=Δum, x∈R+N, t>0, subject to the nonlinear boundary condition −∂um/∂x1=up, x1=0, t>0, in multi-dimension

The role of tool offset on the microstructure and mechanical properties of Al/Cu friction stir welded joints

In this study, dissimilar butt joining of 6061 aluminum alloy and commercially pure copper via friction stir welding was performed with varying tool offset value. The mechanical properties were compared using transverse tensile testing. It was found that as the tool offset decreased from a position of 2 mm–0 mm, the ultimate tensile strength of the welded joint increased, and then decreased drastically when the offset was more than 1.6 mm. X-ray tomography results showed that an effective mechanical interlocking structure was formed with a chaotic interface along the joint line. In addition, in-situ tool temperatures measurement showed that the stir zone peak temperature was highly dependent on tool offset

OHQ: On-chip Hardware-aware Quantization

Quantization emerges as one of the most promising approaches for deploying advanced deep models on resource-constrained hardware. Mixed-precision quantization leverages multiple bit-width architectures to unleash the accuracy and efficiency potential of quantized models. However, existing mixed-precision quantization suffers exhaustive search space that causes immense computational overhead. The quantization process thus relies on separate high-performance devices rather than locally, which also leads to a significant gap between the considered hardware metrics and the real deployment.In this paper, we propose an On-chip Hardware-aware Quantization (OHQ) framework that performs hardware-aware mixed-precision quantization without accessing online devices. First, we construct the On-chip Quantization Awareness (OQA) pipeline, enabling perceive the actual efficiency metrics of the quantization operator on the hardware.Second, we propose Mask-guided Quantization Estimation (MQE) technique to efficiently estimate the accuracy metrics of operators under the constraints of on-chip-level computing power.By synthesizing network and hardware insights through linear programming, we obtain optimized bit-width configurations. Notably, the quantization process occurs on-chip entirely without any additional computing devices and data access. We demonstrate accelerated inference after quantization for various architectures and compression ratios, achieving 70% and 73% accuracy for ResNet-18 and MobileNetV3, respectively. OHQ improves latency by 15~30% compared to INT8 on deployment.Comment: 10 pages, 6 figure

Electron irradiation-induced defects for reliability improvement in monolayer MoS2-based conductive-point memory devices

Monolayer molybdenum disulfide has been previously discovered to exhibit non-volatile resistive switching behavior in a vertical metal-insulator-metal structure, featuring ultra-thin sub-nanometer active layer thickness. However, the reliability of these nascent 2D-based memory devices was not previously investigated for practical applications. Here, we employ an electron irradiation treatment on monolayer MoS2 film to modify the defect properties. Raman, photoluminescence, and X-ray photoelectron spectroscopy measurements have been performed to confirm the increasing amount of sulfur vacancies introduced by the e-beam irradiation process. The statistical electrical studies reveal the reliability can be improved by up to 1.5× for yield and 11× for average DC cycling endurance in the devices with a moderate radiation dose compared to unirradiated devices. Based on our previously proposed virtual conductive-point model with the metal ion substitution into sulfur vacancy, Monte Carlo simulations have been performed to illustrate the irradiation effect on device reliability, elucidating a clustering failure mechanism. This work provides an approach by electron irradiation to enhance the reliability of 2D memory devices and inspires further research in defect engineering to precisely control the switching properties for a wide range of applications from memory computing to radio- frequency switches.This work was supported in part by the National Science Foundation (NSF) grant #1809017, and an NSF MRSEC under Cooperative Agreement No. DMR-1720595. The authors acknowledge use of Texas Nanofabrication Facilities supported by the NSF NNCI award #1542159. D.A. acknowledges the Presidential Early Career Award for Scientists and Engineers (PECASE) through the Army Research Office (W911NF-16-1- 0277).Center for Dynamics and Control of Material

Sulfurization Engineering of One-Step Low-Temperature MoS2 and WS2 Thin Films for Memristor Device Applications

2D materials have been of considerable interest as new materials for device applications. Non-volatile resistive switching applications of MoS2 and WS2 have been previously demonstrated; however, these applications are dramatically limited by high temperatures and extended times needed for the large-area synthesis of 2D materials on crystalline substrates. The experimental results demonstrate a one-step sulfurization method to synthesize MoS2 and WS2 at 550 °C in 15 min on sapphire wafers. Furthermore, a large area transfer of the synthesized thin films to SiO2/Si substrates is achieved. Following this, MoS2 and WS2 memristors are fabricated that exhibit stable non-volatile switching and a satisfactory large on/off current ratio (103–105) with good uniformity. Tuning the sulfurization parameters (temperature and metal precursor thickness) is found to be a straightforward and effective strategy to improve the performance of the memristors. The demonstration of large-scale MoS2 and WS2 memristors with a one-step low-temperature sulfurization method with simple strategy to tuning can lead to potential applications such as flexible memory and neuromorphic computing.This research was primarily supported by the National Science Foundation through the Center for Dynamics and Control of Materials: an NSF MRSEC under Cooperative Agreement No. DMR-1720595. The work was partly done at the Texas Nanofabrication Facility supported by NSF grant NNCI-2025227. This work was performed in part at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Los Alamos National Laboratory, an affirmative action equal opportunity employer, is managed by Triad National Security, LLC for the U.S. Department of Energy’s NNSA, under contract 89233218CNA000001.Center for Dynamics and Control of Material