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Yu-Heng Hong

Publications and source records attributed to Yu-Heng Hong.

3 recordsLinked to original sources

Silicon Photonic Beam Steerer Based on Metalens Focal Plane Array

Focal plane arrays (FPAs) promise robust solid-state beam steering for LiDAR and free-space optical communications. However, the need for external collimation lenses hinders chip-scale compactness. Discrete switching between FPA elements further introduces blind spots and limits the number of resolvable points, restricting applications that require continuous tracking. Here, we demonstrate a silicon photonic beam steerer based on a metalens FPA that monolithically integrates the collimation lens on-chip. Thermo-optic prisms enable continuous fine-tuning, eliminating blind spots and tripling the number of resolvable points. Continuous steering over a 62{\deg} field of view is achieved while maintaining high beam quality, with an average sidelobe suppression ratio of 19 dB.

physics.optics

High-Bandwidth 940 nm VCSEL with Zn-diffusion for Optical Communications

We present a systematic design methodology, combining simulation and experimental validation, for high-speed 940 nm vertical-cavity surface-emitting lasers (VCSELs). A comprehensive simulation study was conducted to optimize the device structure, focusing on the number of oxide layers and the aperture size, which predicted a maximum modulation bandwidth of over 35 GHz. To validate this approach, an optimized device with a 4-{\mu}m double-oxide aperture was fabricated and characterized. Crucially, during the fabrication process, a Zn-diffused region was incorporated to further enhance device performance. The experimental results demonstrate a modulation bandwidth of 34 GHz and successful 100 Gbit/s PAM-4 data transmission. The excellent agreement between the simulated and measured performance validates the effectiveness of our design meth-odology, providing a reliable framework for developing next-generation optical inter-connects.

physics.optics

Mastering the Craft of Data Synthesis for CodeLLMs

Large language models (LLMs) have shown impressive performance in \emph{code} understanding and generation, making coding tasks a key focus for researchers due to their practical applications and value as a testbed for LLM evaluation. Data synthesis and filtering techniques have been widely adopted and shown to be highly effective in this context. In this paper, we present a focused survey and taxonomy of these techniques, emphasizing recent advancements. We highlight key challenges, explore future research directions, and offer practical guidance for new researchers entering the field.

cs.SE