Zhican Zhou | Searcharxiv

arXiv2025

Mode Control and Dynamic Population Gratings in Quantum-Dot Lasers

Single-mode operation is essential for integrated semiconductor lasers, yet most solutions rely on regrowth, etched gratings, or other complex fabrication steps that limit scalability. We show that quantum-dot (QD) lasers can achieve stable single-mode lasing through a simple cavity design using dynamic population gratings (DPGs). Owing to the low lateral carrier diffusion of QDs, a strong standing-wave-induced carrier grating forms in a reverse-biased saturable absorber and provides self-aligned, mode-selective feedback not attainable in quantum-well devices. A single-ring laser achieves 46 dB side-mode suppression ratio (SMSR), while a dual-ring Vernier laser delivers ($>$ 46 nm) tuning range and up to 52.6 dB SMSR, with continuous-wave operation up to $80\,^{\circ}\mathrm{C}$. The laser remains single-mode under $-10.6$ dB external optical feedback and supports isolator-free data transmission at 32 Gbps. These results establish DPG-enabled QD lasers as a simple and scalable route to tunable, feedback-resilient on-chip light sources for communication, sensing, and reconfigurable photonic systems.

physics.optics↗

arXiv2025

ENLighten: Lighten the Transformer, Enable Efficient Optical Acceleration

Photonic computing has emerged as a promising substrate for accelerating the dense linear-algebra operations at the heart of AI, yet adoption for large Transformer models remains in its infancy. We identify two bottlenecks: (1) costly electro--optic conversions and data-movement overheads that erode energy efficiency as model sizes scale; (2) a mismatch between limited on-chip photonic resources and Transformer scale, which forces frequent reuse of photonic tensor cores and dilutes throughput gains. To address these challenges, we introduce a hardware--software co-design framework. First, we propose \texttt{Lighten}, a PTC-aware compression flow that post-hoc decomposes each Transformer weight matrix into a low-rank component plus a structured-sparse component aligned to photonic tensor-core granularity, without lengthy retraining. Second, we present \texttt{ENLighten}, a reconfigurable photonic accelerator with dynamically adaptive tensor cores, driven by broadband light redistribution, enabling fine-grained sparsity support and full power gating of inactive parts. On ImageNet, \texttt{Lighten} prunes a Base-scale Vision Transformer by 50\% with $\approx$1\% accuracy drop after only 3 epochs (about 1 hour) of fine-tuning. Deployed on \texttt{ENLighten}, it achieves a $2.5\times$ improvement in energy--delay product over the state-of-the-art photonic Transformer accelerator.

cs.ET↗