Agnostic Validation Test Bench For Efuse Connectivity Verification

In semiconductor industry, validation is an important process to discover design bugs and have it fixed before the product is released. Semiconductor integrated circuit is normally refreshed in yearly cadence and it is crucial to have a short design and validation cycle, without compromising the product quality. Nowadays, validation process often becomes the bottleneck for product readiness. Integrated circuit validation flow has to be improved in order to keep up with the advancement of integrated circuit design flow. In this work, an improvement method on validation flow is discussed, with particular focus on eFUSE (Electric FUSE) connectivity validation. eFUSE is a feature available in integrated circuit which functions as a central storage for important ‘settings’, and distribute them during system boot up process. eFUSE connectivity validation is needed to ensure each intellectual property is able to retrieve the correct eFUSE value. In this work, the concept of agnostic validation test bench for eFUSE connectivity validation is developed and tested the idea of it is to eliminate manual test development effort, improves validation efficiency and promotes reusability across different projects. By using this methodology, eFUSE connectivity validation time is reduced significantly and recorded an improvement of 28%. There is also an average improvement of 65% in eFUSE coverage percentage. In summary, the eFUSE connectivity validation time frame is shortened, without compromising the test quality

Guarantees of Riemannian Optimization for Low Rank Matrix Completion

We study the Riemannian optimization methods on the embedded manifold of low rank matrices for the problem of matrix completion, which is about recovering a low rank matrix from its partial entries. Assume $m$ entries of an $n\times n$ rank $r$ matrix are sampled independently and uniformly with replacement. We first prove that with high probability the Riemannian gradient descent and conjugate gradient descent algorithms initialized by one step hard thresholding are guaranteed to converge linearly to the measured matrix provided \begin{align*} m\geq C_\kappa n^{1.5}r\log^{1.5}(n), \end{align*} where $C_\kappa$ is a numerical constant depending on the condition number of the underlying matrix. The sampling complexity has been further improved to \begin{align*} m\geq C_\kappa nr^2\log^{2}(n) \end{align*} via the resampled Riemannian gradient descent initialization. The analysis of the new initialization procedure relies on an asymmetric restricted isometry property of the sampling operator and the curvature of the low rank matrix manifold. Numerical simulation shows that the algorithms are able to recover a low rank matrix from nearly the minimum number of measurements

Slip of fluid molecules on solid surfaces by surface diffusion

The mechanism of fluid slip on a solid surface has been linked to surface diffusion, by which mobile adsorbed fluid molecules perform hops between adsorption sites. However, slip velocity arising from this surface hopping mechanism has been estimated to be significantly lower than that observed experimentally. In this paper, we propose a re-adsorption mechanism for fluid slip. Slip velocity predictions via this mechanism show the improved agreement with experimental measurements

Nanoparticles for live cell microscopy: A surface-enhanced Raman scattering perspective.

Surface enhanced Raman scattering (SERS) nanoparticles are an attractive alternative to fluorescent probes for biological labeling because of their photostability and multiplexing capabilities. However, nanoparticle size, shape, and surface properties are known to affect nanoparticle-cell interactions. Other issues such as the formation of a protein corona and antibody multivalency interfere with the labeling properties of nanoparticle-antibody conjugates. Hence, it is important to consider these aspects in order to validate such conjugates for live cell imaging applications. Using SERS nanoparticles that target HER2 and CD44 in breast cancer cells, we demonstrate labeling of fixed cells with high specificity that correlates well with fluorescent labels. However, when labeling live cells to monitor surface biomarker expression and dynamics, the nanoparticles are rapidly uptaken by the cells and become compartmentalized into different cellular regions. This behavior is in stark contrast to that of fluorescent antibody conjugates. This study highlights the impact of nanoparticle internalization and trafficking on the ability to use SERS nanoparticle-antibody conjugates to monitor cell dynamics

Identification of significant factors for air pollution levels using a neural network based knowledge discovery system

Artificial neural network (ANN) is a commonly used approach to estimate or forecast air pollution levels, which are usually assessed by the concentrations of air contaminants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, and suspended particulate matters (PMs) in the atmosphere of the concerned areas. Even through ANN can accurately estimate air pollution levels they are numerical enigmas and unable to provide explicit knowledge of air pollution levels by air pollution factors (e.g. traffic and meteorological factors). This paper proposed a neural network based knowledge discovery system aimed at overcoming this limitation in ANN. The system consists of two units: a) an ANN unit, which is used to estimate the air pollution levels based on relevant air pollution factors; b) a knowledge discovery unit, which is used to extract explicit knowledge from the ANN unit. To demonstrate the practicability of this neural network based knowledge discovery system, numerical data on mass concentrations of PM2.5 and PM1.0, meteorological and traffic data measured near a busy traffic road in Hangzhou city were applied to investigate the air pollution levels and the potential air pollution factors that may impact on the concentrations of these PMs. Results suggest that the proposed neural network based knowledge discovery system can accurately estimate air pollution levels and identify significant factors that have impact on air pollution levels

Conductance spectra of metallic nanotube bundles

We report a first principles analysis of electronic transport characteristics for (n,n) carbon nanotube bundles. When n is not a multiple of 3, inter-tube coupling causes universal conductance suppression near Fermi level regardless of the rotational arrangement of individual tubes. However, when n is a multiple of 3, the bundles exhibit a diversified conductance dependence on the orientation details of the constituent tubes. The total energy of the bundle is also sensitive to the orientation arrangement only when n is a multiple of 3. All the transport properties and band structures can be well understood from the symmetry consideration of whether the rotational symmetry of the individual tubes is commensurate with that of the bundle

Low-Power Random Access for Timely Status Update: Packet-based or Connection-based?

This paper investigates low-power random access protocols for timely status update systems with age of information (AoI) requirements. AoI characterizes information freshness, formally defined as the time elapsed since the generation of the last successfully received update. Considering an extensive network, a fundamental problem is how to schedule massive transmitters to access the wireless channel to achieve low network-wide AoI and high energy efficiency. In conventional packet-based random access protocols, transmitters contend for the channel by sending the whole data packet. When the packet duration is long, the time and transmit power wasted due to packet collisions is considerable. In contrast, connection-based random access protocols first establish connections with the receiver before the data packet is transmitted. Intuitively, from an information freshness perspective, there should be conditions favoring either side. This paper presents a comparative study of the average AoI of packet-based and connection-based random access protocols, given an average transmit power budget. Specifically, we consider slotted Aloha (SA) and frame slotted Aloha (FSA) as representatives of packet-based random access and design a request-then-access (RTA) protocol to study the AoI of connection-based random access. We derive closed-form average AoI and average transmit power consumption formulas for different protocols. Our analyses indicate that the use of packet-based or connection-based protocols depends mainly on the payload size of update packets and the transmit power budget. In particular, RTA saves power and reduces AoI significantly, especially when the payload size is large. Overall, our investigation provides insights into the practical design of random access protocols for low-power timely status update systems

Evaluation of the in vivo anti-inflammatory activity of a flavone glycoside from Cancrinia discoidea (Ledeb.) Poljak

The anti-inflammatory effect of selagin-7-O-(6″-O-Acetyl-)-β-D-glucoside, isolated from the medicinal herb Cancrinia discoidea (Ledeb.) Poljak, was evaluated for its anti-inflammatory activity in the carrageenin- and serotonin-induced rat paw oedema models of acute inflammation and the cotton pellet-induced granuloma rat model of chronic inflammation. Flavone glycoside at doses of 5, 10, or 20 mg/kg, the clinical anti-inflammatory indo-methacin at 10 mg/kg, or vehicle were administered orally before injection of the pro-inflammatory compound. The test compound showed significant anti-inflammatory activity against paw edema induced by carrageenin or serotonin, most notably at the highest test dose of 20 mg/kg. In the cotton pellet-induced granuloma model, the compound showed dose-dependent anti-inflammatory activity, with the highest effect at 20 mg/kg. In all three assays, the flavone glucoside compound was more active at 20 mg/kg than indomethacin at 10 mg/kg