Future lithography systems must produce more dense chips with smaller feature sizes, while maintaining throughput comparable to today's optical lithography systems. This places stringent data-handling requirements on the design of any maskless lithography system. Today's optical lithography systems transfer one layer of data from the mask to the entire wafer in about sixty seconds. To achieve a similar throughput for a direct-write maskless lithography system with a pixel size of 25 nm, data rates of about 10 Tb/s are required. In this paper, we propose an architecture for delivering such a data rate to a parallel array of writers. In arriving at this architecture, we conclude that pixel domain compression schemes are essential for delivering these high data rates. To achieve the desired compression ratios, we explore a number of binary lossless compression algorithms, and apply them to a variety of layers of typical circuits such as memory and control. The algorithms explored include ...

This thesis develops work on using Hidden Markov Models to insert tags natural language text. A taxonomy of tags is developed unifying the fields of text segmentation tagging, part-of-speech tagging, proper noun extraction and hierarchical entity extraction. The search spaces for inserting tags are examined from both a theoretical and experimental point of view across the taxonomy and on four corpora. A analysis of different correctness measures for different types of tag insertion problem is undertaken and a technique to determine whether tag-insertion errors are the result of a modelling failure or a searching failure is discovered.

Text mining involves the use of statistical and machine learning techniques to learn structural elements of text in order to search for useful information in previously unseen text. The need for these techniques have emerged out of the rapidly growing information era. Token identification is an important component of any text mining tool. The accomplishment of this task enhances the function of diverse applications involving searching for patterns in textual data. Several different identification methods have been reported in the literature. HMMs and PPM models have been successfully used in language processing tasks. They have also been applied separately to learning-based token identification. Most of the existing systems are domain- and language-dependent. In this thesis, we implement a system that bridges the two well known methods through words new to the identification model. The system is fully domain- and language-independent. No changes of code are necessary when applying to other domains or languages. The only thing required is an annotated corpus. The system has been tested on two corpora and achieved an overall F-measure of 76:59% for TCC, and 69:02% for BIB. This is not as good as would be expected from a system which includes language-dependent components. However, our system is more generalized. The identification of date has the best result, 73% and 92% of correct tokens are identified respectively. The system also performs reasonably well on people's name with correct tokens of 68% for TCC, and 76% for BIB. ii Acknowledgements During the time of my MPhil. study, I have been so lucky to have had a huge amount of help in academic, financial and personal from a number of people. First and foremost, I would like to thank my chief supervisor, Ian Witte...

Dummy fill is introduced into sparse regions of a VLSI layout to equalize the spatial density of the layout, improving uniformity of chemical-mechanical planarization (CMP). It is now well-known that dummy fill insertion for CMP uniformity changes the back-end flow with respect to layout, parasitic extraction and performance analysis. Of equal import is dummy fill’s impact on layout data volume and the manufacturing handoff. For future mask and foundry flows, as well as potential maskless (direct-write) applications, dummy fill layout data must be compressed at factors of 25 or greater. In this work, we propose and assess a number of lossless and lossy compression algorithms for dummy fill. Our methods are based on the building blocks of JBIG approaches- arithmetic coding, soft pattern matching, pattern matching and substitution, etc. We observe that the fill compression problem has a unique “one-sided ” characteristic; we propose a technique of achieving one-sided loss by solving an asymmetric cover problem that is of independent interest. Our methods achieve substantial improvements over commercial binary image compression tools especially as fill data size becomes large.

Future lithography systems must produce more dense chips with smaller feature sizes, while maintaining throughput comparable to today’s optical lithography systems. This places stringent data-handling requirements on the design of any maskless lithography system. Today’s optical lithography systems transfer one layer of data from the mask to the entire wafer in about sixty seconds. To achieve a similar throughput for a direct-write maskless lithography system with a pixel size of 25 nm, data rates of about 10 Tb/s are required. In this paper, we propose an architecture for delivering such a data rate to a parallel array of writers. In arriving at this architecture, we conclude that pixel domain compression schemes are essential for delivering these high data rates. To achieve the desired compression ratios, we explore a number of binary lossless compression algorithms, and apply them to a variety of layers of typical circuits such as memory and control. The algorithms explored include the Joint Bi-Level Image Processing Group (JBIG), and Ziv-Lempel (LZ77) as implemented by ZIP. In addition, our own extension of Ziv-Lempel to two-dimensions (2D-LZ) is implemented and shown to outperform ZIP compression for all layout data tested. For these layouts, at least one of the above schemes achieves a compression ratio of 20 or larger, demonstrating the feasibility of the proposed system architecture. iii