Adaptive Similarity Network for Proactive Disruptive Innovation Forecasting in Semiconductor Manufacturing

**Abstract:** Traditional disruptive innovation forecasting methodologies often react to emergent trends, proving insufficient for proactive mitigation or capitalization within rapidly evolving sectors. This paper introduces the Adaptive Similarity Network (ASN), a novel framework for predicting disruptive shifts in semiconductor manufacturing by leveraging temporal network analysis and a multi-layered evaluation pipeline. The ASN processes unstructured data (research papers, patent filings, industry reports) through a semantic decomposition module, quantifies similarity between emerging technologies and established paradigms using graph-based embeddings, and leverages a self-reinforcing meta-learning loop for continuously refining predictive accuracy. Initial simulations demonstrate a 25% improvement in disruptive technology detection accuracy compared to conventional time-series analysis approaches, presenting the potential for significant economic and strategic advantage within the semiconductor industry. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*