Multimodal LLM Calibration for DUT Waveform Measurement
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Solution Overview
Problem
Existing machine learning systems for device under test (DUT) calibration and measurement require re-training or building separate models for different measurement, calibration, or characterization parameters, limiting their adaptability and efficiency.
Innovation Solution
A domain-adapted multimodal large language model (MLLM) that integrates text, audio, and image modalities, allowing it to understand and generate accurate calibration, characterization, and measurement parameters without re-training, by aligning text and image encoders through domain-specific training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional machine learning system is used for DUT calibration and measurement, then the system can perform specific measurement tasks, but it requires re-training or building separate models for different measurement parameters, reducing adaptability
Solution Approach 1:
The patent implements a universal ML system that can handle multiple measurement, calibration, and characterization parameters using a single model architecture. The system uses a common feature extraction pipeline that processes waveforms into tensors, which can then be fed into the same ML model to predict different parameters (such as signal quality metrics, calibration values, or characterization data) without requiring separate models for each parameter type.
Solution Approach 2:
The patent segments the measurement system into distinct functional modules: a waveform acquisition module, a feature extraction module that creates tensor representations, and a prediction module. This segmentation allows the feature extraction to be independent and reusable across different measurement tasks, while the prediction layer can be configured for different parameters without affecting the core feature extraction pipeline.
2Measurement precision
If separate ML models are built for different measurement parameters, then each model can be optimized for its specific task, but the overall system complexity increases and training time multiplies
Solution Approach 1:
The patent performs preliminary feature extraction from the waveform data into tensor representations before the prediction stage. This pre-processing step creates a standardized feature set that captures essential characteristics of the signal, which can then be reused across different measurement tasks. By extracting features once and storing them in a standardized format, the system avoids redundant processing when measuring different parameters.
Solution Approach 2:
The patent merges multiple measurement tasks into a unified ML framework where a single model can predict different parameters from the same feature representation. The system combines various measurement objectives (signal quality assessment, calibration, characterization) into one integrated model that processes waveforms through a common feature extraction pipeline and outputs multiple types of measurements simultaneously or sequentially without retraining.
3Measurement precision
If the ML system is re-trained for each new measurement parameter, then the system maintains accuracy for that parameter, but productivity and efficiency decrease
Solution Approach 1:
The patent creates a universal ML model that can predict multiple measurement and calibration parameters from a single feature representation. The model architecture is designed to handle different output types (continuous values, categorical classifications, or calibration parameters) within a unified framework, allowing the system to maintain measurement accuracy across different parameter types without requiring separate specialized models for each.
Data Source
AI summary
A test and measurement instrument includes a port to allow the test and measurement instrument to connect to a device under test (DUT) to receive signals from the DUT, one or more analog-to-digital converters (ADCs) to receive a signal from the DUT and convert the signal to one or more digital waveforms, a user interface to allow a user to enter a query, and one or more processors configured to execute code that causes the one or more processors to: build one or more images of the one or more digital waveforms from the one or more ADCs, send the one or more images to a domain-adapted multimodal large language model (MLLM), receive parameters from the domain-adapted MLLM, provide the user with parameters for the DUT in response to the query, and apply the parameters to the DUT.