LCOS-SLM Based Intelligent Hybrid Algorithm for Beam Splitting
Abstract
:1. Introduction
2. Description of the Algorithms
2.1. The Iterative Fourier Transform Algorithms
- Combine the amplitude of the incident beam with a random phase to form the complex amplitude. Then, take the Fourier transform of the complex amplitude.
- Combine the phase distribution of the Fourier domain with the target amplitude distribution to form a new complex amplitude. Then, perform an inverse Fourier transform on the complex amplitude.
- Replace the random phase in step 1 with the phase distribution of the object domain in step 2;
- Repeat steps 1–3 until the merit function is satisfied or the iteration stagnates.
2.2. The Differential Evolution Algorithm
2.3. The Proposed Algorithm
- First, make the necessary zero padding for the initial sampling points, and then use IFTA or SIFTA to optimize until they stagnate. Obtain the complex amplitude in the Fourier domain.
- According to the preset number, select the amplitude distribution in the signal window of the Fourier domain multiple times to form the initial population.
- Quantify the phase distribution in the Fourier domain to obtain and substitute it into the merit function, together with the population. Get a set of evaluation values, A.
- Then subject the population to mutation, crossover, and boundary processing, in sequence.
- Substitute the processed population and into the merit function to calculate the evaluation value, B.
- Compare A and B to select the better individuals from which to form a new population.
- Calculate the evaluation value, C, of the new population and compare it with A. If it becomes worse, continue to perform steps 3–7; otherwise, perform step 8.
- Compare the optimal evaluation value with the target value; if it is satisfied, stop the iteration and output the optimal individual, otherwise replace with the optimal individual and enter the IFTA loop to continue performing steps 1–7.
3. Results
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value |
---|---|
resolution of LCOS | 2160 × 3840 |
pixel size | 3.74 μm |
modulation depth | 2π |
quantitative level | 256 |
the number of effective pixels | 1024 |
zero-padding multiples | 10 |
the number of sample points | 10,240 |
the waist of the input beam | 0.96 μm |
the waist of the output beam | 0.15 μm |
output beam peak power ratio | 5:4 |
Algorithm | ||
---|---|---|
IFTA | 94.87% | 0.1328 |
SIFTA | 84.47% | 0.0393 |
Source of Initial Population | ||
---|---|---|
IFTA | 93.34% | 0.0559 |
SIFTA | 85.37% | 0.0244 |
Source of Initial Population | ||
---|---|---|
IFTA | 92.00% | 0.0517 |
SIFTA | 84.84% | 0.0194 |
IFTA | SIFTA | Proposed Algorithm | |
---|---|---|---|
1 | 95.11% and 0.1123 | 85.20% and 0.0362 | 85.11% and 0.0221 |
2 | 95.18% and 0.0991 | 87.21% and 0.0419 | 87.13% and 0.0251 |
3 | 94.79% and 0.1061 | 84.00% and 0.0418 | 84.24% and 0.0206 |
4 | 95.19% and 0.1040 | 84.33% and 0.0565 | 84.33% and 0.0222 |
5 | 95.08% and 0.1037 | 83.95% and 0.0415 | 83.95% and 0.0204 |
average | 95.07% and 0.0150 | 84.94% and 0.0436 | 84.95% and 0.0221 |
criterion | 100% and 100% | 89.34% and 41.53% | 89.35% and 21.05% |
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Zhang, X.; Chen, G.; Zhang, Q. LCOS-SLM Based Intelligent Hybrid Algorithm for Beam Splitting. Electronics 2022, 11, 428. https://doi.org/10.3390/electronics11030428
Zhang X, Chen G, Zhang Q. LCOS-SLM Based Intelligent Hybrid Algorithm for Beam Splitting. Electronics. 2022; 11(3):428. https://doi.org/10.3390/electronics11030428
Chicago/Turabian StyleZhang, Xiaoyu, Genxiang Chen, and Qi Zhang. 2022. "LCOS-SLM Based Intelligent Hybrid Algorithm for Beam Splitting" Electronics 11, no. 3: 428. https://doi.org/10.3390/electronics11030428
APA StyleZhang, X., Chen, G., & Zhang, Q. (2022). LCOS-SLM Based Intelligent Hybrid Algorithm for Beam Splitting. Electronics, 11(3), 428. https://doi.org/10.3390/electronics11030428