Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 42,
  • Issue 8,
  • pp. 2868-2879
  • (2024)

Optical Linearization of Silicon Photonic Ring-Assisted Mach-Zehnder Modulator

Not Accessible

Your library or personal account may give you access

Abstract

In high-performance RF photonic systems, the Electro-Optic (EO) modulators play a critical role as a key component, requiring low SWaP-C and high linearity. While traditional lithium niobate (LiNbO $_{3}$ ) Mach-Zehnder Modulators (MZMs) have been extensively utilized due to their superior linearity, silicon-based EO modulators have lagged behind in achieving comparable performance. This paper presents an experimental demonstration of a Ring Assisted Mach Zehnder Modulator (RAMZM) fabricated using a silicon photonic foundry process, addressing the performance gap. The proposed RAMZM modulator enables linearization in the optical domain and can be dynamically reconfigured to linearize around user-specified center frequency and bias conditions, even in the presence of process variations and thermal crosstalk. An automatic reconfiguration algorithm, empowered by Digital-to-Analog Converters (DACs), Analog-to-Digital Converters (ADCs), Trans-Impedance Amplifiers (TIAs), and a digital configuration engine, is developed to achieve linearization, resulting in a spurious-free dynamic range (SFDR) exceeding 113 dB.Hz $^{2/3}$ . Furthermore, a biasing scheme is introduced for RAMZMs, significantly enhancing the modulation slope efficiency, which in turn yields a tone gain of over 13 dB compared to its standard operation. This reconfigurable electro-optic modulator can be seamlessly integrated into integrated RF photonic System-on-Chips (SoCs), leveraging the advantages of integration and cost-effectiveness.

PDF Article

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.