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In advanced embedded split-gate SuperFlash® 3rd generation technology (ESF3) the select gate is compiled with continually scaled core logic transistors. In doing so, enhanced performance and lower power are achieved. However, it was observed during ESF3 process development and integration with 1 V core logic that the program disturb performance was degraded over previous generations of this cell. Data are presented to show that the disturb phenomenon was driven by trap-assisted tunneling in the 19 A core oxide. Corrective actions taken to eliminate this failure mechanism are discussed. Process improvement solutions were successfully applied to the smaller ESF3 technology nodes compatible with 1 V core logic and thinner gate dielectric. Pathway for continual scaling of the ESF3 cell technology in line with core transistors is presented.

It has long been understood that stratification of patients into fine-grained cohorts is a foundation of accurate diagnosis and effective treatment of complex diseases, such as cancer. Nevertheless, cancer therapies still fail or cause unnecessary suffering to many patients, which suggests that our current understanding of cancer sub-types needs to be refined. In this paper, we propose CLIGEN, a novel computational pipeline for high-throughput data-driven stratification of patients with a complex disease into cohorts corresponding to multi-modal disease sub-types based on clinical and genomic data. We applied CLIGEN to discover breast cancer sub-types based on the clinical and genomic data of 503 patients with breast ductal carcinoma in the Cancer Genome Atlas (TCGA). Quantitative and qualitative evaluation of the breast cancer sub-types discovered by CLIGEN indicate that they are biologically meaningful and correlate with clinical outcomes, such as patient survival time.

We performed a comprehensive analysis of the voltage-to-erase (Verase) distribution in SST ESF3 split-gate flash memory cell arrays. It was shown that Verase distribution is mostly determined by the tunneling voltage variations. Other factors, such as distributions of coupling ratios and FG channel parameters, have only a minor effect on Verase variability.

Using the precisely measured floating gate capacitance, we were able to extract the absolute values of programming efficiency in the 40-nm SuperFlash® memory cells at different voltage and temperature conditions. Due to the split-gate design, the cell shows very fast and efficient programming. It was shown that the peak injection efficiency may reach 10%.

Big data is fast becoming a ubiquitous term in both academia and industry and there is a strong need for new data-centric workflow tools and techniques to process and analyze large-scale complex datasets that are growing exponentially. On the other hand, the unbound resource leasing capability foreseen in the cloud facilitates data scientists to wring actionable insights from the data in a time and cost efficient manner. In the data-centric workflow environment, scheduling data processing tasks onto appropriate resources are often driven by the constraints provided by the users. Enforcing a constraint while executing the workflow in the cloud adds a new optimization challenge on how to meet the objective while satisfying the given constraint. In this paper, we propose a new Big dAta woRkflow schEduler uNder budgeT constraint known as BARENTS that supports high-performance workflow scheduling in a heterogeneous cloud computing environment with a single objective to minimize the workflow makespan under a provided budget constraint. Our case study and experiments show the competitive advantages of our proposed scheduler. The proposed BARENTS scheduler is implemented in a new release of DATA VIEW, one of the most usable big data workflow systems in the community.

While big data workflows haven been proposed recently as the next-generation data-centric workflow paradigm to process and analyze data of ever increasing in scale, complexity, and rate of acquisition, a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. In the meanwhile, although NoSQL has emerged as a new category of data models, they are optimized for storing and querying of large datasets, not for ad-hoc data analysis where data placement and data movement are necessary for optimized workflow execution. In this paper, we propose a NoSQL data model that: 1) supports high-performance MapReduce-style workflows that automate data partitioning and data-parallelism execution. In contrast to the traditional MapReduce framework, our MapReduce-style workflows are fully composable with other workflows enabling dataflow applications with a richer structure, 2) automates virtual machine provisioning and deprovisioning on demand according to the sizes of input datasets, 3) enables a flexible framework for workflow executors that take advantage of the proposed NoSQL data model to improve the performance of workflow execution. Our case studies and experiments show the competitive advantages of our proposed data model. The proposed NoSQL data model is implemented in a new release of DATAVIEW, one of the most usable big data workflow systems in the community.

A systematic study of dominant program-disturb mechanisms in advanced embedded split-gate SuperFlash memory across ambient temperature ranging from -45C to 175C is presented. At low temperatures program disturb is initiated by trap-assisted band-to-band tunneling in the split-gate channel area and/or trap-assisted tunneling via thin select gate oxide and at high temperatures - by surface generation in select-gate channel. Effects of single traps on program disturb in split-gate memory have been analyzed. Good quality of thin select gate oxide and its interface with channel is important to meet stringent requirements of the wide-temperature embedded memory applications.

Clustering is a task of finding natural groups in datasets based on measured or perceived similarity between data points. Spectral clustering is a well-known graph-theoretic approach, which is capable of capturing non-convex geometries of datasets. However, it generally becomes infeasible for analyzing large datasets due to relatively high time and space complexity. In this paper, we propose Multi-level Approximate Spectral (MAS) clustering to enable efficient analysis of large datasets. By integrating a series of low-rank matrix approximations (i.e., approximations to the affinity matrix and its subspace, as well as those for the Laplacian matrix and the Laplacian subspace), MAS achieves great computational and spacial efficiency. MAS provides a general framework for fast and accurate spectral clustering, which works with any kernels, various fast sampling strategies and different low-rank approximation algorithms. In addition, it can be easily extended for distributed computing. From a theoretical perspective, we provide rigorous analysis of its approximation error in addition to its correctness and computational complexity. Through extensive experiments we demonstrate superior performance of the proposed method relative to several well-known approximate spectral clustering algorithms.

Substantial amount of research has been done recently to address the shimming problem in scientific workflows, in which a special kind of adaptors, called shims, are inserted between workflow tasks to resolve the data type incompatibility issue. Recently, scientific workflows are increasingly used for big data analysis and processing, which poses additional challenges, such as volume, velocity and variety of data to the shimming problem. One issue is to scale the registration and configuration procedure to a large number of workflow tasks. Another issue is the ease of integrating a large number of remote Web services and other heterogeneous task components that can consume and produce data in various formats and models into a uniform and interoperable workflow. Existing approaches fall short in usability and scalability in addressing these issues. In this paper we 1) propose a new simplified single-component based task model based on extensive experiences and lessons learned from our original multiple-component based task model. The new model separates registration from configuration and eases the process of registering external functional components (such as Web services) into p-workflows, 2) propose a shim generation algorithm that elegantly solves the shimming problem raised by Web service based scientific workflows, and 3) we integrate MongoDB, a NoSQL document-oriented database system for storing and managing large-scale unstructured documents. A new version of the DATAVIEW system has been developed to support the proposed techniques and a case study has been conducted to show the feasibility and usability of our proposed techniques.

A new simple and fast method for separation of cycling-induced degradation components in split-gate SuperFlash® cell is proposed. The method is based on the effect of tunneling current stabilization during linearly ramped erase voltage.

The processes of trap generation and electron trapping in tunnel oxide of SuperFlash® memory cells have been analyzed. It was shown that electrons were mostly trapped by newly generated traps rather than by as-fabricated ones. Due to strongly localized tunneling in SuperFlash cell, it was possible to detect the events of the generation of individual oxide traps. The direct electrical evidence of both single- and double-charge electron trap generation has been observed.

Intrinsic charge-gain program disturb mechanism in split-gate flash memory cells has been identified based on simulation results and experimental data obtained on memory arrays fabricated with 0.18 mum SuperFlashreg technology. It was shown that program disturb has the same nature under all three program disturb conditions existing in NOR flash memory array, and is a result of band-to-band tunneling caused by high electric field in the split-gate channel area and subsequent hot electron injection. We also analyzed reliability aspects of this program disturb mechanism on 16-Mbit memory arrays, and found no substantial effect of 10 program-erase cycles on disturb characteristics. The understanding of intrinsic program disturb mechanism is important for split-gate cell technology scaling as well as for optimization of cell design and operating conditions.

A simple technique for monitoring floating gate (FG) charge gain/loss with elementary charge accuracy is proposed. The technique does not require nano-scale FG or low temperature and can be applied to virtually any submicron FG memory cell. The potential applications include precise capacitance measurements, as well as analysis of program, erase, disturb and data retention characteristics of FG memory cells in extremely low range of FG current (down to 10?23A and below).

Program/erase endurance characteristics of split-gate SuperFlashreg memory cells are discussed. Various factors which affect memory endurance, including cycling data pattern, cycling frequency, temperature, erase retries, and technology scaling, are investigated. Superior data retention after endurance cycling is demonstrated

Erase instabilities and erase performance degradation due to single-electron trapping events in tunnel oxide of SST split-gate SuperFlash/spl trade/ memory cells have been detected and analyzed for the first time. Whereas the instabilities of erase characteristics in stacked-gate flash memories ("erratic erase") are attributed to hole trapping/detrapping associated with anode hole injection, SuperFlash/spl trade/ cell does not show any hole-related processes in tunnel oxide. A different behavior of SuperFlash/spl trade/ cell compared to conventional stacked-gate cell during erase operation due to different cell structures has been analyzed.