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Generation of Large-area Arrays of Aperiodic Functional Micro/nano Structures Using Phase Shift Interferometry

Pearly Princess J., Alfred Kirubaraj A., Christina Sophia S., Senith S., Jino Ramson S. R.

International Journal of Electronics and Telecommunications

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2022

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Vol. 68, No. 1

91--96

EN

Phase shift interferometry (PSI) derived from interference technique as greater surface characterization technique based on the interference information recorded during a controlled phase shift. This research shows the development of micro/nano structures using phase shift interferometry. (PSI) is the process of developing the complex pattern structure using variable phase angle between two or more beams aligned to obtain functional aperiodic arrays. We have designed and modelled the PSI and simulated through MATLAB in 2D and 3D pattern structures. The PSI was performed in two process analysis. First, without PSI referring normal interference technique. Second, with PSI referring position of laser beams in quadrant-based alignment. The obtained results show the minimum feature structure was measured as 12 nm. This feature size developed under phase shift interferometry (PSI) produces minimum feature size compared to the existing interferometry technique. This study gives the promising increased fabrication area could develop large area arrays structures.

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From Data to Pattern Structures : Near Set Approach

Wolski Marcin, Gomolińska Anna

Fundamenta Informaticae

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2019

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Vol. 165, nr 3-4

363--380

EN

Pattern structures were introduced by Ganter and Kuznetsov in the framework of formal concept analysis (FCA) as a mean to direct analysis of objects having complex descriptions, e.g., descriptions presented in the form of graphs instead of a set of properties. Pattern structures actually generalise/replace the original FCA representation of the initial information about objects, that is, formal contexts (which form a special type of data tables); as a consequence, pattern structures are regarded in FCA as given (in some sense a priori to the analysis) rather than built (a posteriori) from data. The main goal of this paper is twofold: firstly, we would like to export the idea of pattern structures to and consistently with the framework/methodology of rough set theory (RST); secondly, we want to derive pattern structures from simple data tables rather than to regard them as the initial information about objects. To this end we present and discuss two methods of generating non-trivial pattern structures from simple information systems/tables. Both methods are inspired by near set theory, which is a methodology theoretically close to rough set theory, but developed in the topological settings of (descriptive) nearness of sets. Interestingly, these methods bear formal connections to other ideas from RST such as generalised decisions or symbolic value grouping.

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Relations between Proto-fuzzy Concepts, Crisply Generated Fuzzy Concepts, and Interval Pattern Structures

Pankratieva V. V., Kuznetsov S. O.

Fundamenta Informaticae

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2012

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Vol. 115, nr 4

265-277

EN

Relationships between proto-fuzzy concepts, crisply generated fuzzy concepts, and pattern structures are considered. It is shown that proto-fuzzy concepts are closely related to crisply generated fuzzy concepts in the sense that the mappings involved in the definitions coincide for crisp subsets of attributes. Moreover, a proto-fuzzy concept determines a crisp subset of attributes, which generates a (crisply generated) fuzzy concept. However, the reverse is true only in part: given a crisp subset of attributes, one can find a proto-fuzzy concept whose intent includes (but not necessarily coincides with) the given subset of attributes. Interval pattern concepts are shown to be related to crisply generated formal concepts. In particular, every crisply closed subset of objects is an extent of an interval pattern concept. Also, we establish some properties of the collection of formal concepts for a given fuzzy context.