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.
Funding
Funding not disclosed
Founders
Product
Problem
Semiconductor and advanced material fabs generate massive amounts of process data, yet only a small portion is analyzed, leaving critical signals undiscovered. Quality checks are typically performed after production, so defects are identified too late to be corrected during growth.
Solution
Atomscale delivers a platform that captures 100% of fab process data and transforms it into a context‑rich intelligence layer. By unifying tool logs, metrology, and characterization, the system builds digital twins of each material growth and provides real‑time visibility into material state. Adaptive process control uses AI models to predict material properties with higher accuracy, enabling automatic adjustments during synthesis. Domain‑specific AI agents act as copilots, offering natural‑language queries, diagnostics, and automation to accelerate development cycles and improve yields. The platform integrates with existing fab infrastructure, delivering continuous insights without requiring extensive manual effort.
Target Audience
Primary customers are semiconductor and advanced materials manufacturers, including process engineering teams, data science groups, and fab operations managers seeking real‑time process intelligence and automation.
Features
- Full‑stack data pipeline that connects, unifies, and enriches all tool and instrument data for real‑time observability
- AI‑driven digital twins that model material state and predict properties during live growths
- Adaptive process control that automatically adjusts synthesis parameters based on predicted material responses
- Domain‑specific AI agents that provide natural‑language querying, root‑cause diagnosis, and workflow automation
- Structured, physics‑rich datasets generated for data‑science teams, ready for downstream analysis
- Seamless integration and configuration services that handle data source connections, model deployment, and validation against metrology