Accelerating Materials Innovation.

WHAT MATERIALSAI DOES

• Builds physics-informed AI models that learn from experimental and simulation data (DFT, KMC, process logs) to predict material properties before fabrication.
• Accelerates material discovery by narrowing the design space for doping profiles, defect control, and layer stack configurations.
• Enables virtual experimentation - allowing engineers to test hundreds of process conditions without running physical wafers.
• Continuously improves with each experiment, creating a proprietary material intelligence layer for the organization.

TARGET CUSTOMERS

Advanced materials Entreprise labs and startups, Semiconductor R&D labs, Defense electronics manufacturers, and Research Institutes working on SiC, GaN, and next-generation materials.

KEY APPLICATIONS

• Fe-doped GaN buffer layer optimization for RF devices
• SiC epitaxy defect reduction and doping control
• Interface engineering for GaN/oxide reliability
• Rapid prototyping of new material stacks for power and RF applications

WHAT FABVERSEAI DOES

• Connects to fab data streams and builds predictive models for yield, defect density, and process drift - calibrated to each fab’s specific equipment configuration
• Delivers real-time process adjustment recommendations to engineers before yield loss occurs
• Identifies root causes of variability across interconnected process steps - not just individual tools
• Continuously improves with every wafer run, building fab-specific intelligence over time

TARGET CUSTOMERS

Semiconductor Fabs, Tool Builders, Pilot line operators at Research institutes, and Equipment vendors building AI-enabled process modules.

KEY APPLICATIONS

• Memory and Logic Chips production
• SiC MOSFET and Schottky diode production
• GaN-on-SiC HEMT epitaxy
• EV inverter supply chain qualification