Ming C. Wu (Advisor)

Research Advised by Professor Ming C. Wu

BPN961: Photonic Integrated Circuits for Scalable Trapped Ion Quantum Computing

Daniel Klawson
Arkadev Roy
Rohan Kumar
2023

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...

BPN751: Large-Scale Silicon Photonic MEMS Switch with Sub-Microsecond Response Time

Johannes Henriksson
Jianheng Luo
2023

We developed a new architecture suitable for building a large-scale optical switch with fast response time. We have demonstrated switches with a scale of 240x240 and speed of sub microsecond using our new architecture. The switch architecture consists of an optical crossbar network with MEMS-actuated couplers and is implemented on a silicon photonics platform. To our knowledge this is the largest monolithic switch, and the largest silicon photonic integrated circuit, reported to date. The passive matrix architecture of our switch is fundamentally more scalable than that of multistage...

Sirui Tang

Graduate Student Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
Ph.D. 2028 (Anticipated)

Joseph Suh

Graduate Student Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
Ph.D. 2028 (Anticipated)

Chun-Yuan Fan

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

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...

Arkadev Roy

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

BPN986: Integrated Microlens Coupler for Photonic Integrated Circuits

Jianheng Luo
Johannes Henriksson
2023

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

BPN991: Autolabeling for Large-Scale Detection Datasets

Philip L. Jacobson
2023

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...

Philip L. Jacobson

Graduate Student Researcher
Electrical Engineering and Computer Sciences
Professor Ming C. Wu (Advisor)
Ph.D. 2024 (Anticipated)

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.