- Research Article
- 10.1117/1.jom.6.1.014501
Artificial neural network–assisted temperature compensation for micro-mirrors
- Feb 04, 2026
- Journal of Optical Microsystems
- Kenta Nakazawa + 2 more +2
Microelectromechanical system (MEMS) mirrors scan light beams and are used in displays and imaging devices in various environments. In particular, in recent years, their application in light detection and ranging has been promoted for autonomous driving of transportation equipment. The driving characteristics of MEMS mirrors are influenced by the driving environment and various physical parameters, especially the temperature. Therefore, developing driving methods that are less affected by the driving environment is necessary. Recently, data-driven artificial neural networks (ANNs) that can extract outputs from various input parameters have also been investigated. Therefore, we propose an operational method for temperature compensation using an ANN for MEMS mirrors. Multiple temperature sensors were installed on a circuit board mounted on a commercially available MEMS mirror to measure the temperature of the operating environment. A dataset was obtained to train and test the constructed ANN. The ANN-based driving method was compared with that using the least-squares method. In addition, the time required to update the parameters in the setup used in this study was investigated.
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