Cofounder
Atomscale
Atomscale offers a platform that captures, unifies, and enriches 100 % of semiconductor fab process data into a context‑rich intelligence layer, enabling real‑time analytics, anomaly detection, and digital twins of material growths. Its domain‑specific AI agents act as copilots, providing natural‑language querying, automated diagnostics, and adaptive process control that adjusts synthesis parameters on the fly, accelerating development cycles and improving yields for advanced materials manufacturers.
- Artificial Intelligence
- AI Agents
- Data & Analytics
- New Materials
- Semiconductor
- Software Only
Updated 5 months ago
Funding
Funding not disclosed
Funding rounds are not available yet.
Founders
Product
Problem
Semiconductor fabs generate massive amounts of process data, yet only a small portion is analyzed, leaving critical signals undiscovered. Traditional quality checks are performed after production, causing delays in detecting and correcting material growth issues, which hampers yield, scale‑up speed, and reliable manufacturing of complex atomic‑scale materials.
Solution
Atomscale provides a platform that captures, unifies, and enriches 100 % of fab process data into a context‑rich intelligence layer. Real‑time analytics surface anomalies, model material state, and generate digital twins of each growth, enabling adaptive process control that adjusts synthesis parameters on the fly. Domain‑specific AI agents act as copilots, offering natural‑language querying, automated diagnostics, and workflow automation grounded in the unified data. By delivering continuous visibility and predictive insights, the platform accelerates development cycles, improves yields, and reduces the cost of scaling advanced materials.
Target Audience
Primary customers are semiconductor fabs and advanced materials manufacturers, as well as their data and process engineering teams that require real‑time insight and automation for atomic‑scale fabrication.
Features
- Full‑stack data integration that connects tool logs, metrology, and characterization to create a unified, physics‑rich dataset
- Real‑time anomaly detection and material‑state modeling with digital twins for each growth
- Adaptive process control that automatically adjusts synthesis parameters based on predicted material properties
- Domain‑specific AI agents that provide natural‑language query, diagnostics, and automation of engineering tasks
- Continuous, layer‑by‑layer feedback during live growths to enable rapid root‑cause analysis and correction
- End‑to‑end implementation support, including data source connection, model configuration, and validation against existing metrology