Characteristics of temporal dynamics of liquid crystal spatial modulators as a limitation of the performance of tunable diffractive neural networks
Liquid crystal spatial light modulators are used in a wide range of modern problems in science and technology. These modulators are used to control the amplitude, phase, and direction of propagation of coherent optical radiation in optical information processing systems. However, the influence of the characteristics of the temporal dynamics of liquid crystal spatial light modulators on the performance of information optical systems, including diffractive neural networks, has not been sufficiently studied. The article presents the results of a study of the temporal dynamics of phase modulation of the liquid crystal spatial light modulator SLM-200 (Santec, Japan). Computer-synthesized binary phase diffractive optical elements were used in the experiments, and the characteristics of the temporal dynamics of the optical modulator were measured: 125 ms is the rise time of the diffraction efficiency when displaying diffractive optical elements on the screen; 61.9 ms is the decay time when switching frames. With these characteristics, it is possible to ensure the formation of a variable optical field at a frame display frequency of 2 Hz with an interference level of –17.1 dB. Increasing the frame display frequency leads to the appearance of unavoidable interframe interference, which in turn limits the effective performance of the information system. The results obtained can be useful in designing high-performance optical information processing systems and diffraction neural networks
Authors
- Е. Yu. Zlokazov (ORCID: https://orcid.org/0000-0003-1340-7734)
- Rostislav S. Starikov (ORCID: https://orcid.org/0000-0002-7369-1565)
- E. K. Petrova (ORCID: https://orcid.org/0000-0002-6764-7664)
- A. V. Shifrina (ORCID: https://orcid.org/0000-0001-7816-5989)
- A. A. Volkov (ORCID: https://orcid.org/0009-0008-4213-9373)
- T. Z. Minikhanov (ORCID: https://orcid.org/0000-0002-2246-9729)
Institutions
- National Research Nuclear University MEPhI (RU)
Publication Details
- Journal
- Izmeritel`naya Tekhnika
- Published
- 2025-04-03
- DOI
- https://doi.org/10.32446/0368-1025it.2025-1-83-89
- Citations
- 1
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.48