A Text Extraction-Based Smart Knowledge Graph Composition for Integrating Lessons Learned during the Microchip Design

TL;DR

This paper presents a dynamic knowledge graph approach that extracts and interlinks information from microchip production documents to improve searchability and access to relevant lessons learned during chip design.

Contribution

It introduces a novel method combining text mining and graph construction to enhance information retrieval in microchip manufacturing documentation.

Findings

Improved retrieval of design failure cases

Enhanced access to production-relevant information

Supports search and recommendation functionalities

Abstract

The production of microchips is a complex and thus well documented process. Therefore, available textual data about the production can be overwhelming in terms of quantity. This affects the visibility and retrieval of a certain piece of information when it is most needed. In this paper, we propose a dynamic approach to interlink the information extracted from multisource production-relevant documents through the creation of a knowledge graph. This graph is constructed in order to support searchability and enhance user's access to large-scale production information. Text mining methods are firstly utilized to extract data from multiple documentation sources. Document relations are then mined and extracted for the composition of the knowledge graph. Graph search functionality is then supported with a recommendation use-case to enhance users' access to information that is related to the initial documents. The proposed approach is tailored to and tested on microchip design-relevant documents. It enhances the visibility and findability of previous design-failure-cases during the process of a new chip design.