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

**Project merged with BPNX1012 on 08/14/2024**

BPNX1010: Foundry-Compatible Silicon Photonic MEMS Switch

Arkadev Roy
Erik Anderson
Daniel Klawson
Yiyang Zhi
Sirui Tang
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

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...

Joseph Suh

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

BPNX1012: Optimization of Integrated Microlens Couplers for Wafer-Scale Packaging

Sirui Tang
Jianheng Luo
Johannes Henriksson
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 goal is to further improve the performance and enhance the fabrication robustness of our IMCs. Specifically, we are replacing the polymer with hard material and developing a design that balances rotational and...

BPN961: Integrated Photonics for Scalable Trapped Ion Quantum Computing

Daniel Klawson
Yiyang Zhi
Arkadev Roy
Rohan Kumar
2024

Photonic integrated circuits (PICs) play a pivotal role in scaling trapped ion quantum systems. However, current quantum PICs suffer from low ion densities. We present a novel quantum PIC for individual optical control of closely-spaced trapped ion qubits. Our device achieves effectively achromatic beam focusing from 405 nm to 810 nm (and beyond) via a planar waveguide lens and a 3D-printed biconic mirror. Moreover, we have measured 30 dB crosstalk at a 5 µm pitch for the 532 nm and 729 nm barium and calcium gate wavelengths, surpassing the state-of-the-art. Finally, our monolithic surface...

Jianheng Luo

Alumni
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
Ph.D. 2024

Jianheng Luo is currently working towards his Ph.D. degree at the University of California, Berkeley with Prof. Ming Wu. He received his B.S. degree in Engineering Physics from the University of California, Berkeley in 2017.

Philip L. Jacobson

Alumni
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
Ph.D. 2024

Philip is currently a second year Ph.D. student working in Prof Ming Wu's group on novel architectures for Machine Learning using Integrated Photonics.

Fall 2023 Research Review Presenter

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...