Silicon photonic modulator circuit with programmable intensity and phase modulation response
Hong Deng, Yu Zhang, Xiangfeng Chen, Wim Bogaerts

TL;DR
This paper introduces a programmable silicon photonic modulator circuit capable of independently controlling intensity and phase modulation, enhancing adaptability and performance in optical communication and sensing applications.
Contribution
It presents a novel circuit-level programmable modulator design that can generate both intensity and phase modulation without platform constraints, demonstrated on silicon photonics platforms.
Findings
Demonstrates precise control over intensity and phase modulation
Surpasses traditional modulators in adaptability and performance
Enables self-calibration with on-chip monitors
Abstract
Electro-optical modulators are essential components in optical communication systems. They encode an electrical waveform onto an optical carrier. However, their performance is often limited by inherent electro-optic processes and imperfections in existing integrated designs, which limit their adaptability to diverse applications. This paper presents a circuit-level programmable modulator design that addresses these challenges. The proposed modulator can generate both intensity and phase modulation, optimizing performance without altering the underlying design or constraining platform limitations. We explain and demonstrate the principle with both carrier depletion-based modulators and SiGe electro-absorption modulators on a silicon photonic platform. Experiments demonstrate precise control and optimization capabilities surpassing those of traditional modulator designs, marking a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPhotonic and Optical Devices · Advanced Photonic Communication Systems · Neural Networks and Reservoir Computing
