IBM

About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Signal. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.

Damascene Copper electroplating for chip interconnections

Abstract: Damascene Cu electroplating for on-chip metallization, which we conceived and developed in the early 1990s, has been central to IBM's Cu chip interconnection technology. We review here the challenges of filling trenches and vias with Cu without creating a void or seam, and the discovery that electrodeposition can be engineered to give filling performance significantly better than that achievable with conformal step coverage. This attribute of superconformal deposition, which we call superfilling, and its relation to plating additives are discussed, and we present a numerical model that represents the shape-change behavior of this system.

New types of deep neural network learning for speech recognition and related applications: an overview

Abstract: In this paper, we provide an overview of the invited and contributed papers presented at the special session at ICASSP-2013, entitled “New Types of Deep Neural Network Learning for Speech Recognition and Related Applications,” as organized by the authors. We also describe the historical context in which acoustic models based on deep neural networks have been developed. The technical overview of the papers presented in our special session is organized into five ways of improving deep learning methods: (1) better optimization; (2) better types of neural activation function and better network architectures; (3) better ways to determine the myriad hyper-parameters of deep neural networks; (4) more appropriate ways to preprocess speech for deep neural networks; and (5) ways of leveraging multiple languages or dialects that are more easily achieved with deep neural networks than with Gaussian mixture models.

The Real-Time Specification for Java

Abstract: New languages, programming disciplines, operating systems, and software engineering techniques sometimes hold considerable potential for real-time software developers. A promising area of interest-but one fairly new to the real-time community-is object-oriented programming. Java, for example, draws heavily from object orientation and is highly suitable for extension to real-time and embedded systems. Recognizing this fit between Java and real-time software development, the Real-Time for Java Experts Group (RTJEG) began developing the real-time specification for Java (RTSJ) in March 1999 under the Java Community Process. This article explains RTSJ's features and the thinking behind the specification's design. The goal of the RTJEG, of which the authors are both members, was to provide a platform-a Java execution environment and application program interface (API)-that lets programmers correctly reason about the temporal behavior of executing software.

On the design and quantification of privacy preserving data mining algorithms

Abstract: The increasing ability to track and collect large amounts of data with the use of current hardware technology has lead to an interest in the development of data mining algorithms which preserve user privacy. A recently proposed technique addresses the issue of privacy preservation by perturbing the data and reconstructing distributions at an aggregate level in order to perform the mining. This method is able to retain privacy while accessing the information implicit in the original attributes. The distribution reconstruction process naturally leads to some loss of information which is acceptable in many practical situations. This paper discusses an Expectation Maximization (EM) algorithm for distribution reconstruction which is more effective than the currently available method in terms of the level of information loss. Specifically, we prove that the EM algorithm converges to the maximum likelihood estimate of the original distribution based on the perturbed data. We show that when a large amount of data is available, the EM algorithm provides robust estimates of the original distribution. We propose metrics for quantification and measurement of privacy-preserving data mining algorithms. Thus, this paper provides the foundations for measurement of the effectiveness of privacy preserving data mining algorithms. Our privacy metrics illustrate some interesting results on the relative effectiveness of different perturbing distributions.

A research manifesto for services science

Abstract: The services sector has grown over the last 50 years to dominate economic activity in most advanced industrial economies, yet scientific understanding of modern services is rudimentary Here, we argue for a services science discipline to integrate across academic silos and advance service innovation more rapidly