Bibliographic Data-Driven Keyword Network Analysis in ReRAM Research

Recently, data informatics has revolutionized the paradigm of scientific research, traditionally based on experiments, formulations, and simulations. Because this type of research is based on the quality and quantity of data, humans have accumulated experimental, formal, and simulation data in various storage formats. Among these, materials databases have become essential repositories of material properties from experiments and simulations, crucial for data-driven research. Despite their growth, much scientific research data remains stored in journal articles written in natural language. Scientific research using journal articles is increasing, and research trend analysis using bibliographic information is no exception. In this study, we propose a method for trend analysis that automatically analyzes the research field by using bibliographic information. Our method involves sequential steps: searching bibliographic information, extracting keywords from text data, and constructing a keyword network. We validated our method in the field of resistive switching memory (ReRAM) and identified different keyword clusters that can categorize the research trend of ReRAM field. Consequently, our method successfully curated the uptrend in neuromorphic computing, demonstrating its potential for insightful analysis in other scientific research fields.