Material Science • Thin Film Deposition • Process Data Modeling • AI-Assisted Process Design
Predictive Process Design from Lab Data
Cavosh trains AI models on real lab process data — surfacing hidden patterns and turning experimental records into actionable guides for process design.
69th Annual SVC TechCon · Long Beach, CA · April 25–30
Can AI Predict Your PVD Process Parameters?
From Historical Data to Predictive Process Windows
Dr. Helia Jalili
April 30, 2026 Long Beach, CA
PVD process development is still largely iterative — yet coating systems generate data continuously. This talk presents a structured path from raw historical records to predictive process guidance, grounded in real production environments.
“Helia’s presentation was eye-opening in clarity and precision — perfectly suited to executives and technologists woefully unprepared for the advantages AI can bring to their workplace.”
The Opportunity
Your process data
already contains
the answer.
1000+ 0
Experiments run Model built from them
Most labs run hundreds of experiments. The relationships between process parameters and material outcomes are buried in spreadsheets and never extracted.
Cavosh finds them. We train predictive models directly on your experimental history — so the next time you need to hit a target property, you start from knowledge, not intuition.
"Experimental records become predictive knowledge."
Engagement Model
Four stages.
No wasted cycles.
STAGE 01
Diagnose
You learn exactly what your data can and cannot predict — before any modeling begins. We assess data quality, structure, and coverage.
Go / No-Go Gate
STAGE 02
Model
We build the feature set around your process: pressure, reactive gases, bias, temperature, timing, and all relevant physical constraints.
Go / No-Go Gate
We only advance when the work earns it. Every stage ends with a Go / No-Go gate — so you never fund a model that doesn't meet the bar.
STAGE 03
Prove
The model is trained and tested against historical runs. We evaluate predictions against agreed technical criteria until the model is decision-useful.
Go / No-Go Gate
STAGE 04
Deliver
The model is trained and tested against historical runs with traceability back to the supporting data.
The Cavosh Innovation Method
From target properties to optimal process
Performance Specs
Desired material properties:
color · hardness · resistivity
Target Performance → Process Parameters
AI Learning Engine
AI iteratively learns
parameter–outcome relationships
Lab Process Data
Historical lab data:
deposition · pressure · metrology
Predictive Model
Process window recommended
with full data traceability
Why Cavosh Innovation
Built for teams that need engineering-grade answers.
Cavosh Innovation applies machine learning to thin film deposition data, helping engineers predict process parameters from desired material properties.
-
Years of experience in thin film deposition and characterization. The models are built with an understanding of sputtering processes, materials behavior, and real lab constraints.
-
No synthetic benchmarks. No toy demos. Every model trained on your actual experimental history.
-
We only advance when the work earns it. You never pay for a model that doesn't meet the bar.