Ming C. Wu (Advisor)

Research Advised by Professor Ming C. Wu

BPN986: Integrated Microlens Coupler for Photonic Integrated Circuits

Jianheng Luo
Johannes Henriksson
2024

We design and experimentally demonstrate a new silicon photonic fiber coupling method using integrated microlens couplers. Efficient, broadband and polarization-insensitive coupling to a single mode fiber with a best coupling loss of 0.8 dB is achieved.

Project currently funded by: Industry Sponsor

Arkadev Roy

Postdoctoral Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)

Arkadev Roy obtained his Ph.D. Degree in electrical engineering from California Institute of Technology in 2023. He completed his BTech degree in Electronic and Electrical communication engineering from the Indian Institute of Technology Kharagpur in 2018. Currently, he is a postdoc scholar at UC Berkeley and his work focuses on opto-electro-mechanical systems and heterogeneous integration.

BPNX1012: Optimization of Integrated Microlens Couplers for Wafer-Scale Packaging (New Project)

Sirui Tang
2024

Despite the current widespread use of silicon photonics, fiber coupling remains one of the principal challenges in mass production. Integrated microlens couplers (IMCs) have been demonstrated as an efficient, broadband, and polarization-insensitive method for wafer-scale fiber-to-chip coupling, with a previously achieved free-space coupling loss of 0.6 dB. In this project, our objective is to further optimize the performance of IMCs by addressing current sources of loss and fabrication limitations. Potential research directions include optimizing the waveguide for single-mode behavior,...

BPNX1010: Foundry-Compatible Silicon Photonic MEMS Switch (New Project)

Arkadev Roy
2024

Integrated silicon photonic switches can serve as primary building blocks for low-latency, high-bandwidth interconnects for communication in data-intensive scenarios ranging from servers in datacenters to chiplets in multi-chip integrated packages. Our group has been developing MEMS-based large-scale silicon photonic switches which are particularly attractive for their low-loss, high-extinction, and low-power performance as well as sub-microsecond switching speed. Previous demonstrations, although fully compatible with CMOS foundries, relied upon a custom fabrication stack. The goal of the...

BPNX1009: Piezoelectric Silicon Photonic MEMS Switch (New Project)

Joseph Suh
2024

Integrated silicon photonic switches enable routing of optical signals and real-time reconfiguration of the optical networks in data centers and high performance computers. Reducing the operating voltage while preserving the required switching properties is vital to realize their full potential for real-time routing driven by CMOS electronics. Silicon photonic MEMS switches, based on electrostatic actuation, have employed high voltages because of constraints in mechanical designs aimed at eliminating parasitic effects. On the other hand, recent developments in piezoelectric thin...

BPN961: Integrated Photonics for Scalable Trapped Ion Quantum Computing

Daniel Klawson
Arkadev Roy
Yiyang Zhi
Rohan Kumar
2024

Quantum computing is a new paradigm of computing that promises exponential performance increases for certain tasks as compared to classical computers. Trapped ions have been identified as a favorable medium – trapped ion quantum computers perform operations on singular atoms with precisely aimed laser pulses calibrated to state transitions within the ions’ energy levels. Bulk free space optics are currently used for qubit manipulation, but the large amount of optical equipment required hinders scalability. Recent pushes to build higher bit systems have identified photonic integrated...

Chun-Yuan Fan

Postdoctoral Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
PostDoc 2023

Chun-Yuan Fan received Ph.D. in Photonics and Optoelectronics from National Taiwan University in 2021. He is a professional in photonics and optical system design. His doctoral research field was related to metasurface with an optimization algorithm for advanced optical systems, including the electrically modulated metalens, ultrawide angle, and broadband achromatic metalens. He also established and designed a deep learning system for the company. After his Ph.D., he did a half-year postdoc at the original lab and proposed advanced optimization for the optical component, such as the...

BPN991: Autolabeling for Large-Scale Detection Datasets

Philip L. Jacobson
2024

3D perception is an essential task for autonomous driving, and thus building the most accurate, computationally efficient, fast, and label efficient models is of great interest. In particular, label-efficient 3D detection is attractive as manual labeling of 3D LiDAR point clouds is both costly and time-consuming. Autolabeling is a machine learning paradigm in which a model is trained on a (small) set of labeled data before being used to generate predictions, known as pseudo-labels, on a large set of unlabeled data which can then be used to train an accurate downstream model with only a...

Yiyang Zhi

Graduate Student Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
Ph.D. 2027 (Anticipated)