Di Liang

In this work, we introduce a non‐volatile heterogeneous quantum dot (QD) III‐V/Al ₂ O ₃ /Si distributed feedback (DFB) laser exhibiting optical memristive behavior. The device operates in the O‐band (∼1300 nm) with a threshold current density of 234 A/cm ² and a side‐mode suppression ratio exceeding 48 dB. Co‐integrated Al ₂ O ₃ ‐based memristors produce bipolar resistive switching, yielding non‐volatile wavelength shifts of ∼46 pm and ∼ 17 dB peak power contrast with zero static holding power. The III‐V/Al ₂ O ₃ /Si heterojunction memristor I – V hysteresis is also modeled. This new device enables simultaneous coherent light generation and persistent optical state storage, establishing a new class of active photonic memory for neuromorphic and reconfigurable wavelength division multiplexing (WDM) applications.

In this work, we report on the design strategies and experimental validation of ultra-low threshold (2 mW (at 10°C) for 1.3 μm emission. The multi-dimensional design exploration allows for the demonstration of record wall-plug efficiencies (∼10 % ) and threshold current densities (109 A/ cm ² ) among electrically driven, silicon-waveguide-coupled microring lasers. We also demonstrate the thermal performance of several designs with record characteristic temperature values of T ₀ =212 K, indicating minimal temperature dependence of the threshold current. In addition, the high differential gain allows for the demonstration of 3 dB bandwidths up to 5 GHz.

As an increasingly powerful technique in integrated photonics, inverse design uses optimization algorithms to automatically create compact, high-performance photonic structures, often yielding non-intuitive layouts far more compact than conventional designs [J. S. Jensen and O. Sigmund, Laser Photonics Rev. 5, 308 (2011) and S. Molesky et al., Nat. Photonics 12, 659 (2018)]. While adjoint-based inverse design is a prominent optimization method, the resulting free-form layouts are difficult to interpret or diagnose under fabrication variability [C. M. Lalau-Keraly et al., Opt. Express 21, 21693 (2013); L. Su et al., arXiv:1910.04829 [physics.app-ph] (2019); and A. Y. Piggott et al., Nat. Photonics 9, 374 (2015)]. We present an experimentally validated interpretability workflow that produces pixel-level sensitivity maps directly on the binary mask of an inverse-designed device. Using wavelength-division demultiplexers at 1310/1550 nm as examples, we train a lightweight convolutional surrogate to regress figures of merit and apply integrated gradients to attribute predicted transmission to individual pixels. We demonstrate that high-attribution hotspots correspond to physically meaningful substructures, such as splitter hubs and high-curvature edges. Experimental results show that controlled perturbations in these high-sensitivity regions result in up to an ∼11× higher excess insertion loss compared to perturbations in non-sensitive regions, consistent with full-wave simulations. This approach adds a practical explainability layer to existing pipelines, offering a clear pathway toward foundry-compatible design-rule checking, targeted quality control, localized metrology, and fabrication-aware constraint allocation without modifying the underlying electromagnetic solver.

Mid and deep ultraviolet (UV) laser diodes remain among the least explored devices in semiconductor optoelectronics, despite their importance for spectroscopy, biochemical sensing, disinfection, and emerging quantum photonics. Here, we demonstrate an electrically pumped AlGaN-based laser diode operating in the UV-B band (280–315 nm). The device is grown by molecular beam epitaxy on a single-crystal AlN substrate and fabricated in a ridge-waveguide geometry. The laser diode operates at 298.5 nm and exhibits a relatively low threshold current density of 3.4 kA/cm2. Clear non-linear light–current characteristics and pronounced spectral narrowing with a full width at half maximum of 0.2 nm are measured above the threshold.

Silicon photonics has emerged as a transformative solution to address the energy and bandwidth challenges of modern computing and communication systems. While integrating diverse materials with silicon has enhanced the functionality of photonic integrated circuits, these hybrid approaches often face challenges related to scalability, cost, and compatibility with CMOS processes. We provide a comprehensive perspective on advancing high-performance all-silicon photonic devices and systems. By fully leveraging the inherent potential of silicon, diverse functionalities have been demonstrated, including Raman lasers, modulators, photodiodes, and optical memories. This approach outlines a pathway toward fully integrated electronic–photonic circuits on the silicon platform, seamlessly aligned with the existing CMOS infrastructure.

In this work, we introduce programmable memory elements embedded within III-V/Si light sources which facilitate non-volatile wavelength tuning. These non-volatile III-V/Si micro-ring lasers (MRLs) exhibit non-volatile wavelength shifts of ~80 pm with ~40 dB signal extinction ratio while consuming 0 electrical static tuning power. An array of 5 cascaded MRLs is demonstrated with each laser capable of 4 programmable non-volatile states, thus yielding 1024 unique states altogether. Write/erase operations were performed up to 100 cycles with non-volatile time duration lasting up to 24 h. These non-volatile lasers are used to demonstrate optical ternary content-addressable memories (O-TCAM) which can find utility in fast memory search and in-memory computing functions necessary for various machine learning algorithms. The end-to-end energy consumption of the non-volatile MRL O-TCAM with 5 ternary symbols is 1156 fJ/sym. This work provides an opportunity for realizing photonic memory applications in next generation non-volatile photonic systems.

This paper highlights the critical role of solid-state quantum dot (QD) light sources in both classical and quantum applications, with an emphasis on their integration with silicon photonics to advance future optical networks and quantum technologies. Quantum dot lasers, renowned for their low threshold currents, temperature stability, low-noise optical amplification, and enhanced coherence, are highlighted as essential components for scalable quantum systems. These features contribute to improved chip architectures, reduced module sizes, and increased channel density. The paper also explores the synergy between quantum dot lasers and silicon photonics in the generation of frequency combs, optimizing efficiency and scalability in optical networks. Furthermore, it delves into the impact of quantum dot-based single-photon sources, particularly their ability to generate entangled and polarized photons, in driving advancements across quantum technologies.

Implementing on-chip non-volatile optical memories has long been an actively pursued goal, promising significant enhancements in the capability and energy efficiency of photonic integrated circuits. Here, we demonstrate an non-volatile optical memory exclusively using the most common semiconductor material, silicon. By manipulating the photon avalanche effect, we introduce a trapping effect at the silicon-silicon oxide interface, which in turn demonstrates a non-volatile reprogrammable optical memory cell with a record-high 4-bit encoding, robust retention and endurance. This silicon avalanche-induced trapping memory provides a distinctively cost-efficient and high-reliability route to realize optical data storage in standard silicon foundry processes. We demonstrate its applications in trimming in optical interconnects and in-memory computing. Our in-memory computing test case reduces energy consumption by approximately 83% compared to conventional optical approaches.

The convergence of deep learning and Big Data has spurred significant interest in developing novel hardware that can run large artificial intelligence (AI) workloads more efficiently. Over the last several years, silicon photonics has emerged as a disruptive technology for next-generation accelerators for machine learning (ML). More recently, the heterogeneous integration of III-V compound semiconductors has opened the door to integrating lasers and semiconductor optical amplifiers at wafer-scale, enabling the scaling of the size, density, and complexity of silicon photonic integrated circuits (PICs). Furthermore, using this technology, all of the individual components required to execute the operations within a neural network are available and can be integrated on the same PIC. Here, we review our innovations of an energy-efficient and scalable silicon photonic platform serving as the underlying foundation for next-generation AI accelerator hardware.