Characterization and optimization of copper chemical mechanical planarization

The feature scale planarization of the copper chemical mechanical planarization (CMP) process has been characterized for two copper processes using Hitachi 430-TU/Hitachi T605 and Cabot 5001/Arch Cu10K consumables. The first process is an example of an abrasive-free polish with a high-selectivity barrier slurry, while the second is an example of a conventional abrasive slurry with a low-selectivity barrier slurry. Copper fill planarization has been characterized for structures with conformal deposition as well as with bumps resulting from bottom-up fill. Dishing and erosion were characterized for several structures after clearing. The abrasive-free polish resulted in low sensitivity to overpolish and low saturation levels for dishing and erosion. Consequently, this demonstrated superior performance when compared to the International Technology Roadmap for Semiconductors (ITRS) 2000 roadmap targets for planarization. While the conventional slurry could achieve the 0.13-μm technology node requirements, the abrasive-free polish met the planarization requirements beyond the 0.10-μm technology node.     

On c‐Bhaskar Rao designs with block size 4

Several new families of c‐Bhaskar Rao designs with block size 4 are constructed. The necessary conditions for the existence of a c‐BRD (υ,4,λ) are that: (1)λ_min=?λ/3 ≤ c ≤ λ and (2a) c≡λ (mod 2), if υ > 4 or (2b) c≡ λ (mod 4), if υ = 4 or (2c) c≠ λ ? 2, if υ = 5. It is proved that these conditions are necessary, and are sufficient for most pairs of c and λ; in particular, they are sufficient whenever λ?c ≠ 2 for c > 0 and whenever c ? λ_min≠ 2 for c < 0. For c < 0, the necessary conditions are sufficient for υ> 101; for the classic Bhaskar Rao designs, i.e., c = 0, we show the necessary conditions are sufficient with the possible exception of 0‐BRD (υ,4,2)'s for υ≡ 4 (mod 6). © 2002 Wiley Periodicals, Inc. J Combin Designs 10: 361–386, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/jcd.10009     

MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization.

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently being developed. Index Terms-Improved fuzzy c-means clustering (IFCM), magnetic resonance imaging (MRI), neighborhood attraction, segmentation.     

Robust Dynamics and Control of a Partially Observed Markov Chain

In a seminal paper, Martin Clark (Communications Systems and Random Process Theory, Darlington, 1977, pp. 721–734, 1978) showed how the filtered dynamics giving the optimal estimate of a Markov chain observed in Gaussian noise can be expressed using an ordinary differential equation. These results offer substantial benefits in filtering and in control, often simplifying the analysis and an in some settings providing numerical benefits, see, for example Malcolm et al. (J. Appl. Math. Stoch. Anal., 2007, to appear). Clark’s method uses a gauge transformation and, in effect, solves the Wonham-Zakai equation using variation of constants. In this article, we consider the optimal control of a partially observed Markov chain. This problem is discussed in Elliott et al. (Hidden Markov Models Estimation and Control, Applications of Mathematics Series, vol. 29, 1995). The innovation in our results is that the robust dynamics of Clark are used to compute forward in time dynamics for a simplified adjoint process. A stochastic minimum principle is established.     

Ultrasonic analysis of edible fats and oils

Low intensity ultrasound is a powerful analytical technique for investigating the physico-chemical properties of many biological and non-biological materials. In this article its application for the characterization of edible fats and oils is assessed. Ultrasound can be used to determine the dynamic rheology and composition of oils, the oil content and droplet size of emulsions and the solid fat content of partially crystalline emulsions. It is capable of rapid and precise measurements, is non-destructive and non-invasive, can be used on-line or off-line and is relatively inexpensive. Ultrasonic techniques will therefore prove a useful addition to the existing analytical techniques used to characterize fats and oils.