arXiv subjects

Yiming Pang

Publications and source records attributed to Yiming Pang.

7 recordsLinked to original sources

arXiv2026

A Wafer-Scale Heterogeneous III-V-on-Silicon Nitride Quantum Photonic Platform

Heterogeneous integration of gain and strongly nonlinear materials with ultra-low-loss silicon nitride (SiN) photonics offers a route to scalable quantum circuits, but concurrent wafer-scale manufacturability, low interlayer loss, and high performance have been challenging to realize. Here we demonstrate a wafer-scale III-V-on-SiN quantum photonic platform that directly integrates III-V layers to foundry-fabricated SiN circuits. The SiN layer provides 200-300 nm thick waveguides with $<1$ dB/m loss and a mature passive photonics ecosystem, while III-V materials provide large $\chi^{\left(2\right)}$ and $\chi^{\left(3\right)}$ nonlinearities for parametric gain, frequency conversion and quantum light generation. Adiabatic interlayer couplers yield $<25$ mdB loss to InGaP waveguides and resonators with intrinsic quality factors exceeding $10^6$, enabling $15\times$ brighter entanglement sources and efficient nonlinear conversion on SiN. Integrated components--including low-loss beam splitters, waveguide crossers, and tunable interferometers--are complemented by III-V lasers and InP photodetectors with amplifiers achieving up to $99^{+1}_{-12}\%$ quantum efficiency and $3$ GHz bandwidth. This architecture unites ultra-efficient sources, nonlinear elements and detectors on a wafer-scale, low-loss platform, establishing a path toward large-scale, low-noise quantum photonic systems.

physics.optics↗

arXiv2026

High-Efficiency InGaP-on-Insulator Microresonator Nonlinear Conversion and Entanglement Generation

InGaP-on-insulator (InGaP-OI), with its intrinsically high $\chi^{\left(2\right)}$ optical nonlinearity, has emerged as an efficient and bright integrated photonic platform for frequency conversion and on-chip entanglement generation, but high waveguide propagation loss in the visible wavelength range has limited its overall performance. Here, we identify the dominant loss mechanism through mode-profile analysis and effectively mitigate the loss using a surface treatment method. Statistical analysis of the resonator quality factor and propagation loss reveals the optimal ring radius that maintains a strong nonlinear interaction while suppressing significant bending related loss, resulting in loss as low as 0.49 dB/cm (4.31 dB/cm) at 1560 nm (780 nm). The method provides a 3.5--4$\times$ quality factor enhancement at 780 nm, enabling a second-harmonic generation efficiency of $3.01\times10^{5}$ \,\%/\textrm{W} and a degenerate photon-pair generation rate of $4.27\,\textrm{MHz}/\mu\textrm{W}$ and coincidence-to-accidental ratio as high as 10,000. The quasi-phase matching condition is experimentally verified, and nonlinear conversion is systematically characterized across the entire parameter space. This work establishes a scalable pathway for classical and quantum photonics in a low-loss, highly nonlinear, and wafer-scale integration platform.

physics.app-ph↗

arXiv2026

Programming strain-stiffening in soft composites via structural memory near jamming

Soft composite solids, comprising discrete inclusions embedded within a compliant matrix, are emerging candidates for engineering synthetic tissues and soft robotic materials. Current strategies for controlling their nonlinear mechanics, such as strain-stiffening, have primarily relied on the nonlinear elasticity of polymer matrices. Although direct contacts between inclusions may enhance stiffening responses at high densities, the role of the non-equilibrium and history-dependent nature of disordered contact networks in composite mechanics remains unexplored. In this work, by applying a mechanical training protocol near a shear-jamming phase boundary, we demonstrate that the structural memory encoded in contact networks drives a crossover from granular-like to biopolymer-like strain stiffening. Simulations of a coarse-grained composite model reveal that this biopolymer-like mechanical response emerges from enhanced non-affine reconfigurations of nearly-jammed contact networks. Without relying on matrix nonlinearity, we establish a design strategy that leverages non-equilibrium memory effects intrinsic to granular systems to achieve highly programmable strain-stiffening in soft composites.

cond-mat.soft↗

arXiv2024

A Versatile Chip-Scale Platform for High-Rate Entanglement Generation using an AlGaAs Microresonator Array

Integrated photonic microresonators have become an essential resource for generating photonic qubits for quantum information processing, entanglement distribution and networking, and quantum communications. The pair generation rate is enhanced by reducing the microresonator radius, but this comes at the cost of increasing the frequency mode spacing and reducing the quantum information spectral density. Here, we circumvent this rate-density trade-off in an AlGaAs-on-insulator photonic device by multiplexing an array of 20 small-radius microresonators each producing a 650-GHz-spaced comb of time-energy entangled-photon pairs. The resonators can be independently tuned via integrated thermo-optic heaters, enabling control of the mode spacing from degeneracy up to a full free spectral range. We demonstrate simultaneous pumping of five resonators with up to $50$ GHz relative comb offsets, where each resonator produces pairs exhibiting time-energy entanglement visibilities up to 95$\%$, coincidence-to-accidental ratios exceeding 5,000, and an on-chip pair rate up to 2.6 GHz/mW$^2$ per comb line -- more than 40 times improvement over prior work. As a demonstration, we generate frequency-bin qubits in a maximally entangled two-qubit Bell state with fidelity exceeding 87$\%$ (90$\%$ with background correction) and detected frequency-bin entanglement rates up to 7 kHz ($\sim 70$ MHz on-chip pair rate) using $\sim 250$ $\mu$W pump power. Multiplexing small-radius microresonators combines the key capabilities required for programmable and dense photonic qubit encoding while retaining high pair-generation rates, heralded single-photon purity, and entanglement fidelity.

quant-ph↗

arXiv2024

Advances in high-pressure laser floating zone growth: the Laser Optical Kristallmacher II

The optical floating zone crystal growth technique is a well-established method for obtaining large, high-purity single crystals. While the floating zone method has been constantly evolving for over six decades, the development of high-pressure (up to 1000 bar) growth systems has only recently been realized via the combination of laser-based heating sources with an all-metal chamber. While our inaugural high-pressure laser floating zone furnace design demonstrated the successful growth of new volatile and metastable phases, the furnace design faces several limitations with imaging quality, heating profile control, and chamber cooling power. Here, we present a second-generation design of the high-pressure laser floating zone furnace, "Laser Optical Kristallmacher II" (LOKII), and demonstrate that this redesign facilitates new advances in crystal growth by highlighting several exemplar materials: $α$-Fe$_2$O$_3$, $β$-Ga$_2$O$_3$, and La$_2$CuO$_{4+δ}$. Notably, for La$_2$CuO$_{4+δ}$, we demonstrate the feasibility and long-term stability of traveling solvent floating zone growth under a record pressure of 700 bar.

physics.app-ph↗

arXiv2023

Learning Agility and Adaptive Legged Locomotion via Curricular Hindsight Reinforcement Learning

Agile and adaptive maneuvers such as fall recovery, high-speed turning, and sprinting in the wild are challenging for legged systems. We propose a Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end tracking controller that achieves powerful agility and adaptation for the legged robot. The two key components are (I) a novel automatic curriculum strategy on task difficulty and (ii) a Hindsight Experience Replay strategy adapted to legged locomotion tasks. We demonstrated successful agile and adaptive locomotion on a real quadruped robot that performed fall recovery autonomously, coherent trotting, sustained outdoor speeds up to 3.45 m/s, and tuning speeds up to 3.2 rad/s. This system produces adaptive behaviours responding to changing situations and unexpected disturbances on natural terrains like grass and dirt.

cs.RO↗

arXiv2022

Balancing Value Underestimation and Overestimation with Realistic Actor-Critic

Model-free deep reinforcement learning (RL) has been successfully applied to challenging continuous control domains. However, poor sample efficiency prevents these methods from being widely used in real-world domains. This paper introduces a novel model-free algorithm, Realistic Actor-Critic(RAC), which can be incorporated with any off-policy RL algorithms to improve sample efficiency. RAC employs Universal Value Function Approximators (UVFA) to simultaneously learn a policy family with the same neural network, each with different trade-offs between underestimation and overestimation. To learn such policies, we introduce uncertainty punished Q-learning, which uses uncertainty from the ensembling of multiple critics to build various confidence-bounds of Q-function. We evaluate RAC on the MuJoCo benchmark, achieving 10x sample efficiency and 25\% performance improvement on the most challenging Humanoid environment compared to SAC.

cs.LG↗