Configuration and Reasoning Functions for Knowledge-based VLSI Pattern Design System

我们已经从这样一个观点进行了研究，即基于知识的系统结构和基于该结构的智能功能是先进的VLSI-CAD系统所必需的。对于基于知识的CAD系统。除了迄今为止结合现有知识系统所探索的分析或验证功能之外，还需要自动或半自动合成功能。虽然有几种方法可能是对知识为基础的LSI图案设计系统，我们已经构建了一个原型系统具有功能单元库为核心。功能单元库基于知识结构，并存储一组智能单元数据（组件），以响应请求进行组合。我们的LSI功能单元库系统命名为OCLS目前可以支持NMOS图形设计的Mead-Conway设计方法的基础上。提出了一种模式描述语言，它适用于模式的层次化描述，用变量表示几何模式，并嵌入相关知识。该语言在基于Prolog的面向对象程序设计风格的框架内支持必要的功能。设计规则检查器也集成在系统中。对于不太熟悉OCLS语法的用户，提供了一个语法导向的图形编辑器。在我们的大规模集成电路设计系统中，设计过程基本上是选择必要的单元并将它们组合以产生更大的图案。用户目前可以通过单元格目录显示窗口进行选择。由于我们认为，基于假设的推理可以有利地用于自动化这个选择过程，我们已经利用了增强的知识表示和学习机制的知识库执行基于假设的推理。

We have conducted a research from a viewpoint that a knowledge-based system structure and intelligent functions based on that structure are required toward advanced VLSI-CAD systems. For the knowledge-based CAD system. automatic or semi-automatic synthesis functions are needed in addition to the analysis or verification functions explored so far in connection with existing knowledge systems. Although several approaches may be possible toward a knowledge-based LSI pattern design system, we have constructed a prototype system having a functional cell library as a kernel. The functional cell library is based on a knowledge structure and stores a set of intelligent cell data (components) to be combined in response to the request. Our LSI functional cell library system named OCLS can presently support NMOS artwork pattern design based on the Mead-Conway design methodology. A pattern description language has been developed, which is suitable for a hierarchical pattern description, expressing geometical patterns with using variables, and embedding related knowledge. The language supports necessary functions in the framework of object-oriented programming style based on Prolog. A design rule checker is also integrated in the system. For the users not enough familiar with the syntax of OCLS, a syntax-directed editor with graphics is provided. In our LSI design system, the design proceeds basically as sellecting necessary cells and combining them to produce larger patterns. The selection is done by the user at present with the help of a cell catalog desplay window. Since we consider that a hypothesis-based reasoning can be advantageously used for automating this selection process, we have exploited an enhanced knowledge representation and a learning mechanism for the knowledge-base performing hypothesis-based reasoning.