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Glassware Calibration

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Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...

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Related Experiment Video

Updated: Dec 25, 2025

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

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Published on: April 1, 2020

Automatic calibration of silicon ring-based optical switch powered by machine learning.

Wei Gao, Liangjun Lu, Linjie Zhou

|April 1, 2020

Summary

This summary is machine-generated.

This study introduces an artificial neural network (ANN) for automatic calibration of dual-ring assisted Mach-Zehnder interferometer (DR-MZI) optical switches. The machine learning approach achieves high accuracy and efficiency for tunable optical filters and switch fabrics.

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Area of Science:

Photonics
Optical Engineering
Machine Learning Applications

Background:

Automatic calibration is crucial for large-scale ring-based optical switch fabrics.
Existing calibration methods can be time-consuming and less precise.

Purpose of the Study:

To develop and validate an artificial neural network (ANN) model for the automatic calibration of 2×2 dual-ring assisted Mach-Zehnder interferometer (DR-MZI) switches.
To demonstrate the retrieval of switch parameters from measured spectra using machine learning.

Main Methods:

Development of an artificial neural network (ANN) model.
Utilizing machine learning algorithms to analyze spectral data.
Experimental verification on multiple DR-MZI devices.

Main Results:

The ANN model accurately retrieves switch parameters.
Operating wavelength tuning achieved with <90 pm accuracy.
Extinction ratios >20 dB in <7 calibration cycles.
Voltage difference between automatic and manual tuning <30 mV.

Conclusions:

The proposed ANN-based scheme offers an accurate and efficient method for calibrating DR-MZI optical switches.
This approach is suitable for large-scale optical switch fabrics and tunable optical filters.
Machine learning provides a novel pathway for automated calibration in photonic devices.