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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Characteristics of temporal dynamics of liquid crystal spatial modulators as a limitation of the performance of tunable diffractive neural networks

Е. Yu. Zlokazov, Rostislav S. Starikov, E. K. Petrova, A. V. Shifrina et al.
1 citations
Izmeritel`naya Tekhnika
Neural Networks and Reservoir Computing
2.48
article

Characteristics of temporal dynamics of liquid crystal spatial modulators as a limitation of the performance of tunable diffractive neural networks

Е. Yu. Zlokazov, Rostislav S. Starikov, E. K. Petrova, A. V. Shifrina, A. A. Volkov, T. Z. Minikhanov
article en
1 citations

Abstract

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

Izmeritel`naya TekhnikaVol. 74(1)
National Research Nuclear University MEPhI (RU)
Openalex Percentile: Top 11%
Neural Networks and Reservoir Computing
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.