Scientists turn vibration into a measurement tool for surface metrology
Researchers at Shanghai Jiao Tong University and the State Key Laboratory of Dynamic Optical Imaging and Measurement have developed a new interferometry method that uses mechanical disturbance instead of fighting it. The technique could make high-precision surface measurements more practical in large-optics testing and industrial settings where vibration is hard to eliminate.
Why it matters: - The method could expand high-precision surface metrology beyond vibration-isolated labs. - Large-aperture optics testing and in-situ industrial measurement often face structural vibration, support deformation and other disturbances that weaken conventional phase-shifting interferometry. - The new approach turns those disturbances into usable phase-shifting signals, which could reduce the need for active phase shifters and strict isolation systems.
What happened: - Scientists from the Jiamiao Yang team at Shanghai Jiao Tong University, working with the State Key Laboratory of Dynamic Optical Imaging and Measurement, developed disturbance-introduced interferometry, or DII. - The team paired DII with a natural phase decoding algorithm, or NPDA, to reconstruct nanometer-level surface shapes. - The study was published with DOI 10.37188/lam.2026.090.
The details: - DII treats unavoidable mechanical disturbances as phase-shifting excitation instead of noise. - The method uses random mechanical disturbances such as structural vibration, support deformation and micro-displacement to modulate optical path difference. - That modulation encodes surface phase information into a sequence of interferograms. - NPDA estimates disturbance phase for each frame, filters out undersampled or distorted interferograms, and then refines surface phase through iterative decoupling of spatial and temporal variables. - The workflow removes the need for dedicated phase-shifting devices. - Numerical simulations showed the method stayed robust across a wide disturbance range. - When disturbance phase amplitude reached 7 pi radians, reconstruction error stayed as low as 0.0005 wavelength. - Conventional four-step phase-shifting interferometry failed under the same condition, with errors up to 0.5 wavelength. - Compared with advanced disturbance-resistant algorithms tested in the study, DII improved tolerable disturbance amplitude by more than 20 times. - Experiments under strong vibration, using only a standard office desk for support, produced wavefront repeatability better than 0.0018 wavelength. - The experimental results closely matched conventional phase-shifting interferometry under controlled conditions. - RMS differences were as low as 0.0002 wavelength for spherical surfaces and 0.0007 wavelength for flat surfaces. - Traditional phase-shifting interferometry could not provide reliable measurements under the same dynamic disturbances. - The work was supported by the National Natural Science Foundation of China, the Chinese Academy of Sciences, the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University, the Science and Technology Commission of Shanghai Municipality, the Shanghai Innovation Action Plan Project and the Startup Fund for Young Faculty at SJTU.
Between the lines: - The core shift is conceptual as much as technical: vibration becomes part of the measurement signal rather than a source of error. - That makes the method more practical for environments where engineers cannot fully control mechanical stability. - The results suggest a possible path for interferometric systems that need lab-level precision without lab-level isolation.
What's next: - The approach is positioned for large-aperture optics testing and in-situ industrial metrology. - Broader use will likely depend on how the method performs across more hardware setups and more challenging manufacturing environments. - If the results hold up in deployment, DII could change how interferometric measurements are done in vibration-prone settings.
Disclaimer: This article was produced by AGP Wire with the assistance of artificial intelligence based on original source content and has been refined to improve clarity, structure, and readability. This content is provided on an “as is” basis. While care has been taken in its preparation, it may contain inaccuracies or omissions, and readers should consult the original source and independently verify key information where appropriate. This content is for informational purposes only and does not constitute legal, financial, investment, or other professional advice.
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