РАЗРАБОТКА ПРОТОТИПА ПРОГРАММНОГО МОДУЛЯ АВТОМАТИЗИРОВАННОЙ ГЕНЕРАЦИИ КВАЛИФИКАЦИОННЫХ ЯЧЕЕК ДЛЯ КОНТРОЛЯ ПРАВИЛ ПРОЕКТИРОВАНИЯ ИС

Разработан прототип программного модуля генератора квалификационных ячеек для автоматизированного контроля геометрических правил проектирования DRC. Проведено тестирование прототипа генератора в реальных рабочих условиях проектирования. The paper presents a prototype of software module of the QA-cells Generator for automated Design Rule Checking. The QA-Cells Generator has been tested in the real workplace within actual microelectronic industrial design.

Deep learning-based extraction of predicate-argument structure (PAS) in building design rule sentences☆

This paper describes an approach to extracting a predicate-argument structure (PAS) in building design rule sentences using natural language processing (NLP) and deep learning models. For the computer to reason about the compliance of building design, design rules represented by natural language must be converted into a computer-readable format. The rule interpretation and translation processes are challenging tasks because of the vagueness and ambiguity of natural language. Many studies have proposed approaches to address this problem, but most of these are dependent on manual tasks, which is the bottleneck to expanding the scope of design rule checking to design requirements from various documents. In this paper, we apply deep learning-based NLP techniques for translating design rule sentences into a computer-readable data structure. To apply deep learning-based NLP techniques to the rule interpretation process, we identified the semantic role elements of building design requirements and defined a PAS for design rule checking. Using a bidirectional long short-term memory model with a conditional random field layer, the computer can intelligently analyze constituents of building design rule sentences and automatically extract the logical elements. The proposed approach contributes to broadening the scope of building information modeling-enabled rule checking to any natural language-based design requirements.