Multiscale AI provides a platform that creates Bayesian digital twins of semiconductor fab processes using sparse silicon data and physics simulations, delivering predictive analytics and optimization recommendations in days instead of months. The system centralizes data, automates curation, and offers APIs, SDKs, and visual workflow tools so fab engineers can quickly develop and deploy AI‑driven yield improvement applications.
Funding
$11.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Semiconductor fabs often struggle with fragmented data sources, limited silicon test data, and lengthy development cycles for process optimization tools, leading to slow yield improvements and high operational costs.
Solution
Multiscale AI offers a platform that combines Bayesian digital twins and AI agents to rapidly generate actionable insights from sparse silicon data and physics simulations. By centralizing data, unifying security, and providing automated data curation, the system creates digital twins in days rather than months. The platform’s decision intelligence layer and active learning capabilities continuously refine models, enabling fab engineers to predict process outcomes and implement yield‑enhancing adjustments quickly. Integrated APIs, SDKs, and workflow builders allow custom fab‑ready applications to be deployed at scale, accelerating the overall SMPO workflow.
Target Audience
Primary customers are semiconductor manufacturing fabs and material suppliers seeking to accelerate yield optimization and reduce time‑to‑insight through AI‑driven process analytics.
Features
- Bayesian digital twins that infer process behavior from limited silicon measurements and simulation results
- LLM‑powered AI agents that automate digital twin creation and update cycles in days
- Centralized business semantic layer with Delta Lake integration for unified data access and security
- Automated data curation and active learning pipelines to continuously improve model accuracy
- Decision intelligence layer delivering predictive analytics and optimization recommendations
- Extensible APIs, SDKs, and widget library for rapid development of custom fab‑ready applications
- Visual workflow builders and widget integration for non‑expert users to orchestrate SMPO processes