Use Case
AI Noise Reduction Algorithm for Precision Instruments
AI automatically removes noise from measurement data, enabling nanometer-scale precision.
Overview
We developed an algorithm in which AI learns the noise characteristics of measurement data and removes them automatically. The result: general-purpose noise reduction that absorbs device-to-device differences, and an adaptive design that improves with use.
Benefits
- AI learns noise characteristics and removes them automatically
- Adaptive algorithm that improves in accuracy with continued use
- General-purpose noise reduction that absorbs device variations
Target Industries
- Precision Measurement
- Semiconductors
- Materials
Challenges Before / Changes After
Before
- Noise removal required manual filter tuning
- Achieving nanometer-scale precision was difficult
- Noise characteristics varied per device, making handling individual-dependent
After
- AI learns noise characteristics and removes them automatically
- Adaptive algorithm that improves in accuracy with continued use
- General-purpose noise reduction that absorbs device variations
Implementation
Noise Characterization & Modeling
Analyzes instrument-specific noise characteristics and designs the foundation of the removal model.
Adaptive AI Noise Reduction Algorithm
Built an AI algorithm that continuously learns from in-use data and improves accuracy over time.
Lightweight Embedded Implementation
Delivered a lightweight implementation suitable for embedding into instrument firmware.
Input Formats
Raw instrument data (roughness profiles, vibration signals)
Output Formats
Noise-reduced data, accuracy evaluation reports
Integrations
Instrument firmware, quality management systems
Project Summary
Team & Timeline
- Noise analysis → algorithm development → device integration
- Measurement engineering / firmware development / a.s.ist engineers
- Lightweight design geared for instrument embedding
Outcomes
- Achieved nanometer-scale measurement precision
- Reduced effort through automated noise removal
- Achieved generality across device variations
Contact Us
We propose end-to-end support — from algorithm development through device integration — for noise reduction of measurement data.
- Noise characterization and modeling
- Development of AI noise reduction algorithms
- Lightweight embedded implementation