Optimized Design with Artificial Intelligence Quantum Dot White Mini LED Backlight Module Development
Abstract
:1. Introduction
2. Experiment and Algorithm Design
2.1. Mini-LED Module Design
2.2. Overall Design Workflow
2.3. Definitions of Action Functions, State Functions, and Reward Functions of DDQN Algorithm
- The online and target networks are initialized;
- At each time step, an action is selected from the online network based on the current state of the state space (St);
- The selected action is performed, the next state is observed, and the environmental feedback is rewarded.
- The next action from the target network is selected as an action space (at), and its Q-value is evaluated.
- The Q estimate for the online network is updated using the next state of the feedback and the reward function (rt);
- The target network is regularly updated by copying the parameters from the online network to the target network;
- Steps 2 through 6 are repeated until the desired stop condition is reached.
3. Result
3.1. Divergent Angle of Single Packaged Mini-LED
3.2. Experimental Result for DDQN
3.3. QD Color Conversion and Reliability Test of the Mini-LED Backlight Module
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Symbol | Range |
---|---|---|
Width of transparent layer | a | 30 μm to 200 μm, step: 10 μm |
Thickness of transparent layer | b | 30 μm to 140 μm, step: 10 μm |
Thickness of diffusor layer | c | 30 μm to 140 μm, step: 10 μm |
Weight concentration of TiO2 | d | 0.1% to 30%, step: 0.1% |
State Space (St) | Action Space (at) | Reward Function (rt) | |||
---|---|---|---|---|---|
State No. | State Definition | Action No. | Action Definition | Reward No. | Reward Definition |
S1 | The value of a | a1, a2 | increase a, decrease a | u | Illuminance uniformity = Minimum Illuminance/Average Illuminance |
S2 | The value of b | a3, a4 | increase b, decrease b | r | r1 = (Uniformitynew − 73)3/1000 r2 = (unew − uold)/100 R (out of range) = −10 |
S3 | The value of c | a5, a6 | increase c, decrease c | ||
S4 | The value of d | a7, a8 | increase d, decrease d |
Weight Concentration | 2 | 2.5 | 3 | 3.5 | 4 | 4.5 | 5 | |
---|---|---|---|---|---|---|---|---|
Divergence Angle | ||||||||
L = 0 | 141 | 146 | 152 | 156 | 165 | 166 | 167 | |
L = 90 | 147 | 152 | 161 | 170 | 171 | 168 | 167 |
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Lee, T.-Y.; Huang, W.-T.; Chen, J.-H.; Liu, W.-B.; Chang, S.-W.; Chen, F.-C.; Kuo, H.-C. Optimized Design with Artificial Intelligence Quantum Dot White Mini LED Backlight Module Development. Crystals 2023, 13, 1411. https://doi.org/10.3390/cryst13101411
Lee T-Y, Huang W-T, Chen J-H, Liu W-B, Chang S-W, Chen F-C, Kuo H-C. Optimized Design with Artificial Intelligence Quantum Dot White Mini LED Backlight Module Development. Crystals. 2023; 13(10):1411. https://doi.org/10.3390/cryst13101411
Chicago/Turabian StyleLee, Tzu-Yi, Wei-Ta Huang, Jo-Hsiang Chen, Wei-Bo Liu, Shu-Wei Chang, Fang-Chung Chen, and Hao-Chung Kuo. 2023. "Optimized Design with Artificial Intelligence Quantum Dot White Mini LED Backlight Module Development" Crystals 13, no. 10: 1411. https://doi.org/10.3390/cryst13101411
APA StyleLee, T.-Y., Huang, W.-T., Chen, J.-H., Liu, W.-B., Chang, S.-W., Chen, F.-C., & Kuo, H.-C. (2023). Optimized Design with Artificial Intelligence Quantum Dot White Mini LED Backlight Module Development. Crystals, 13(10), 1411. https://doi.org/10.3390/cryst13101411