Design of a silicon Mach-Zehnder modulator via deep learning and evolutionary algorithms

Area of Science:

• Photonics and Optical Engineering
• Materials Science and Engineering
• Computational Science

Background:

• Silicon Mach-Zehnder modulators (MZMs) are critical components in optical communication, but optimizing their performance for high-speed applications is challenging.
• Conventional optimization methods for MZMs are time-consuming and resource-intensive due to complex device physics and numerous design parameters.

Purpose of the Study:

• To develop a significantly faster and more efficient design methodology for high-performance silicon Mach-Zehnder modulators.
• To overcome the limitations of traditional optimization techniques in achieving optimal MZM configurations.

Main Methods:

• Implemented a deep neural network to replace computationally expensive 3D electromagnetic simulations of silicon MZMs.
• Integrated the neural network model with a heuristic optimization algorithm (differential evolution) to estimate the figure of merit.
• Applied the methodology to optimize CMOS-compatible MZMs for electro-optical bandwidth, insertion loss, and half-wave voltage.

Main Results:

• Achieved optimized MZM configurations with enhanced electro-optical bandwidth and reduced driving voltage.
• Demonstrated specific configurations yielding a [Formula: see text] bandwidth with [Formula: see text] driving voltage, or [Formula: see text] bandwidth with [Formula: see text] driving voltage.
• Enabled faster exploration of the performance boundaries for silicon MZMs under various constraints.

Conclusions:

• The proposed artificial neural network and heuristic optimization methodology drastically reduces MZM design complexity and simulation time.
• This approach facilitates the discovery of novel, high-performance MZM designs crucial for advancing optical communication technologies.
• The method allows for efficient optimization under diverse constraints, pushing the performance limits of silicon MZMs.