Optimal Control of SOAs with Artificial Intelligence for Sub-Nanosecond Optical Switching
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Abstract (arXiv)
Novel approaches to switching ultra-fast semiconductor optical amplifiers using artificial intelligence algorithms (particle swarm optimisation, ant colony optimisation, and a genetic algorithm) are developed and applied both in simulation and experiment. Effective off-on switching (settling) times of 542 ps are demonstrated with just 4.8% overshoot, achieving an order of magnitude improvement over previous attempts described in the literature and standard dampening techniques from control theory.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jun 2020.
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Citing papers per year: 2022: 2; 2023: 1.
3 papers.
- 2022RAMP: A Flat Nanosecond Optical Network and MPI Operations for Distributed Deep Learning Systemscited by 1
- 2022One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learningcited by 0
- 2023Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networkscited by 0
- 2023Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks31 Jan 2023
- 2022RAMP: A Flat Nanosecond Optical Network and MPI Operations for Distributed Deep Learning Systems28 Nov 2022
- 2022One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning25 Oct 2022