Physical AI Brief

We present a variability-aware simulation framework for heterogeneously integrated lithium niobate traveling-wave modulators. The framework incorporates fabrication-variation data obtained from our dedicated pilot line and enables efficient optimisation of geometric parameters to ensure stable device performance across wafer-scale manufacturing. Using this methodology, we theoretically demonstrate that reliable wafer-scale integration of LN modulators on silicon photonics via micro-transfer printing is feasible and can be systematically engineered.

World models have enabled interactive exploration of game environments and robotic manipulation, but physical engineering remains beyond their reach: real materials exhibit nonlinear constitutive laws, carry history-dependent internal state, undergo inertial dynamics, and may possess hierarchical structures spanning multiple length scales. We present LEIA (Learned Environment for Interactive Architected materials), a world model that lets engineers apply boundary conditions step by step and observe the resulting deformation and stress fields in real time. LEIA handles large three-dimensional unstructured meshes and generates autoregressive responses to user-specified loading. We introduce MicroPlate, a benchmark of architected plates spanning two regimes of microstructure modeling: architected lattices that resolve microstructure explicitly through three-dimensional geometry, and a homogeneous plate where microstructural change is modeled implicitly through internal degrees of freedom. MicroPlate is used to assess LEIA alongside four baseline methods across both regimes. Finally, we demonstrate that LEIA enables efficient candidate generation and ranking for fast surrogate-guided search for de novo designs of architected materials, with stress-accurate candidate ranking validated by finite element ground truth.

Photonic integrated circuits have emerged as a scalable platform for optical computing, communication, and quantum technologies, where high-fidelity optical processing is essential. However, as photonic systems scale in complexity, inter-channel crosstalk accumulates across cascaded components, fundamentally degrading signal fidelity, limiting system-level performance, and constraining integration density. Existing crosstalk-suppression strategies rely on specialized nanostructures or platform-specific designs, hindering their adoption in standard foundry processes and across diverse material systems. Here we establish a universal and foundry-compatible route to eliminating crosstalk based on slab-engineered mode hybridization in standard rib waveguides. By tailoring the slab thickness, mode hybridization induces anisotropic modal perturbations that enable complete cancellation of coupling between adjacent waveguides. We experimentally demonstrate zero-crosstalk across diverse material platforms, including silicon-on-insulator, silicon nitride, thin-film lithium niobate, and germanium-on-insulator, spanning wavelengths from the visible to the mid-infrared. Our approach provides a manufacturable route toward scalable, high-fidelity, and high-density photonic integration, overcoming the long-standing trade-off between signal fidelity and integration density in large-scale photonic systems.

We propose a velocity sensor based on a two-memory Mach--Zehnder interferometer fed by a coherent probe and squeezed vacuum, read out by balanced homodyne detection. One memory is taken as a stationary reference, while the second memory moves during storage, so that its velocity is mapped onto a differential interferometric phase at readout. The two memories are otherwise assumed identical and are described by a Gaussian write--store--read lifetime together with the associated unconditional noise floor. Using the classical Fisher information, we derive the velocity sensitivity, the transmission threshold required for a target quantum gain, and the optimum storage time. The squeezed scheme improves on equal-resource coherent homodyne within an operating window set mainly by total transmission and phase stability. For representative near-term parameters, unconditional memory noise floors up to about $10^{-1}$ photons per trial do not by themselves remove the advantage; after optimization the improvement remains at the few-percent level and is limited chiefly by loss.

Light-based advanced manufacturing increasingly requires programmable, closed-loop tools that translate human design intent into executable operations at small length scales. Yet a key bottleneck persists across robotic and manufacturing modalities: turning user intent into machine-readable objectives that are reliably executable. While micro-robotics offers versatile manipulation via optical actuation of fluids, mathematically tractable goal specification remains manual and hard to reuse. Here, we introduce Speak-to-Objective, a modular agentic pipeline that uses a conditioned Large Language Model (LLM) to translate spoken or written commands into fully differentiable objective functions for assembling microparticles in a constraint-aware inverse solver (SLSQP) and on an experimental optofluidic platform. The approach employs a compact loop - perceive -> compose -> propose -> act -> report & learn - that treats the objective as the interface between intent and actuation, separating what to assemble or pattern from how to actuate, while learning from user feedback. The pipeline composes geometry, spacing, and assignment/topology terms to generate robust descriptive objectives that assemble from partial traces and recover after perturbations, as well as explicit objectives for precise placement, all in an actuator-agnostic fashion. Using laser-induced thermoviscous flows as the physical actuation modality, we demonstrate natural-language-programmable, light-based microscale assembly of particle patterns in a microfluidic environment. Beyond its immediate impact on programmable microassembly, and using laser-induced optofluidic actuation as a reduced-complexity experimental platform, our work points toward self-driving, AI-assisted optical manufacturing platforms in which natural language, differentiable objectives, and laser-based actuation are coupled into a reusable digital workflow.

Photonic chip-based frequency combs have emerged as a transformative platform, enabling compact, scalable, and high-performance multiwavelength sources with far-reaching impact across science and technology. Most commonly, these sources leverage the cavity enhancement of the nonlinearities to produce a spectrum of equidistant frequency lines via cascaded four-wave mixing in high-quality microresonators pumped with a continuous wave tone. While the presence of the resonator inherently enables low-power threshold operation, it also brings intrinsic limitations in efficiency, tunability, and power per line. Here, we propose and demonstrate a cavity-less approach for the generation of optical frequency combs on-chip, which relies on non-degenerate cascaded four wave mixing in dispersion engineered integrated photonic waveguides. The results presented here enable previously inaccessible regimes of pump-to-comb conversion efficiency, wide-range continuous line-spacing tunability, and power per line for coherent comb states. This work opens new research opportunities in nonlinear integrated photonics and pathways toward high-capacity optical interconnects, scalable photonic AI accelerators, and other power-constrained integrated systems.

Spin-orbit torque (SOT) enables ultra-fast, energy-efficient magnetization switching, making it a promising mechanism for introducing MRAMs for cache memory applications. However, current SOT-MRAM devices face write efficiency limitations, with charge-to-spin conversion ($ξ_{DL}$) reaching $\sim$ 45\%, far below the projected $\sim$ 80\% needed to comply with the current delivery of advanced transistor nodes. Recent advances in orbital current physics, evidenced in a wide class of materials, offer a path to enhance $ξ_{DL}$. Here, we study the Ta(3-30 nm)\slash W(1-4 nm) system, revealing a large additional spin-orbit torque contribution arising from Ta, a four-fold increase compared to the spin Hall effect in Ta alone, attributed to the orbital Hall contribution. This system exhibits larger $ξ_{DL}$ than W-based SOT systems with more robust perpendicular magnetic anisotropy and compatibility with 400$^\circ$C annealing. Leveraging these advantages, we integrate the Ta/W system into 3-terminal SOT-MTJ devices, showing a level of performance similar to that of W-based systems. Our results show that orbital physics can be easily integrated into SOT-MTJ systems, offering a viable strategy to enhance SOT-MRAM efficiency. In addition, we propose and demonstrate a proof-of-concept for vertical non-local switching of SOT-MTJ using orbital torques, simplifying bottom-pinned SOT-MRAM fabrication.

We investigate magnetoresistance phenomena associated with the magnetization hard-axis collapse in polycrystalline Co thin films. Transport measurements reveal that, for specific orientations of the applied magnetic field, the system exhibits distinct remanent resistance levels in both the in-plane longitudinal and transverse voltage responses. In particular, the planar Hall resistance shows multiple stable and reproducible levels at room temperature, enabling the identification of at least three remanent states that can be distinguished and used for information storage. These resistance levels originate from non-uniform magnetic configurations stabilized after the application and removal of the external magnetic field in the hard-axis region. Since this phenomenon remains largely unexplored, we present an incipient study addressing its potential implications from an applied-physics perspective. The observation of such behavior in polycrystalline Co thin films grown on Si substrates suggests a simple and low-cost platform for spintronic memory and sensing devices based on the remanent planar Hall effect.

Physical reservoir computing offers an energy-efficient route to sequential cognitive inference by outsourcing nonlinear temporal mixing to hardware substrates with rich intrinsic dynamics, with free-space light-scattering systems particularly attractive for their parallelism and reconfigurability-yet practical design principles linking hardware control variables to computational performance have remained unestablished. Here, we establish such principles by systematically mapping three physical control axes of a reconfigurable optoelectronic light-scattering reservoir-reservoir dynamics, input-reservoir coupling, and reservoir interconnectivity-and identifying a quantitative optimum along each axis. Within this design landscape, we observe a memory-capacity peak that coincides with near-zero maximal Lyapunov exponent and is quantitatively reproduced in numerical simulation, extending edge-of-chaos confirmations previously reported in ion-gating and spin-wave reservoirs into the photonic substrate. The two remaining axes exhibit a density-magnitude trade-off in input coupling and an intermediate optimum in reservoir interconnectivity. Operating at the resulting three-axis optimum, the reservoir achieves stable Mackey-Glass chaotic time-series prediction in free-running mode and 84.5% blind classification accuracy on the 10-class Speech Commands spoken-digit benchmark; the principles, stated in substrate-specific units yet rooted in substrate-independent concepts of criticality and balanced coupling, provide a transferable framework for reconfigurable optical reservoir hardware.

Frequency-stable lasers enable high-fidelity quantum state manipulation, which forms the basis of optical atomic clocks, quantum sensing, and quantum computation. Performing state manipulations at increasingly high speeds requires attention to laser frequency noise at high Fourier (carrier-offset) frequencies that cannot be addressed by traditional cavity stabilization alone. Scalable operations also benefit from device miniaturization. Here, we demonstrate a hybrid laser stabilization approach that combines ultrahigh frequency stability of a cryogenic silicon cavity with high-Fourier-frequency noise suppression of an integrated Brillouin laser. The combined system suppresses frequency noise over a Fourier span of more than 7 decades, yielding a <1 Hz phase-integrated linewidth and 0.2 Hz^2/Hz frequency noise at Fourier frequencies above 10 MHz. The performance of this hybrid laser is confirmed by sub-Hz Rabi spectroscopy with a three-dimensional ^{87}Sr lattice clock. This work demonstrates record-low frequency noise at 698 nm over an extensive Fourier frequency range and highlights the promise of precision clock spectroscopy using a chip-scale integrated laser technology.

The Abbe diffraction limit, tied to the fundamental spatial bandwidth constraint imposed by any physical aperture, remains the primary barrier to achieving ultimate far-field optical resolution and precise light-matter interactions. However, current efforts to engineer structured light fields beyond this limit often come at the cost of massive sacrifices in energy efficiency. In this work, we mathematically complete the family of non-zero azimuthal-order Circular Prolate Spheroidal Wave Functions (CPSWFs), introducing them as a complete class of band-limited superoscillatory optical vortices carrying helical phase. Compared with classical Laguerre-Gaussian (LG) beams, we rigorously prove that these eigenmodes achieve the theoretical upper bound for extreme energy concentration under strict band-limited constraints. At the scale of light-matter interactions, this optimal concentration directly amplifies the intensity gradients and angular momentum densities that govern optical forces. This advantage translates directly into a 29.9% reduction in the trapping power threshold and a 2.3-fold increase in the subdiffraction orbital rotation speed of nanoparticles. Looking forward, this fundamental physical framework not only establishes strict mathematical boundaries for structured light fields but also serves as an absolute theoretical benchmark for deep-learning inverse design, and next-generation extreme optical micro-manipulation systems.

The inverse problem of multilayer thin-film optical coatings design represents a complex combinatorial-continuous optimization challenge. We present PRISM (Position-encoded Regressive Inverse Spectral Model), a unified decoder-only autoregressive transformer that streamlines this process by jointly predicting discrete material selection and continuous thickness regression within a single backbone. PRISM introduces two primary architectural innovations: (1) spectrum prefix conditioning, which utilizes standard prefix tokens for in-context target injection, and (2) cumulative-depth Rotary Position Embeddings, which encode continuous thickness directly into the positional representation to preserve the physical spatial relationships of the stack. Our benchmarks demonstrate that a PRISM-13M model reduces MAE by over 50\% compared to other transformer baselines while utilizing only one-fifth of the parameters. Furthermore, a 44M-parameter variant achieves state-of-the-art performance (MAE = 0.010) on our in-distribution validation benchmark and operates significantly faster than simulated annealing, offering a highly efficient alternative to classical optimization methods.

We introduce "Courant", a Perceiver-based encoder-processor-decoder surrogate model that has latent features exhibiting adaptive specialization and local support in the physical space, enabling functionality akin to an adaptive hp-refinement scheme, an attribute that is highly desirable in traditional numerical solvers and scientific machine learning broadly. The proposed architecture combines a shared random Fourier feature coordinate embedding, state-adapted latent queries, and a light-weight decoder. Courant is trained end-to-end with steady or transient simulation data and only a standard L_2 prediction loss in the physical space, achieving competitive accuracy on benchmarks. We demonstrate that Courant's inductive biases yield latents that are interpretable by design: they develop multiscale geometric specialization in the simulation domain and track coherent structures in the time-dependent case, acting analogously to time-evolving spatial basis functions and allowing for decoding a compact, geometry-anchored, partition-of-unity-like decomposition of the simulated field.

A novel silicon photonic CWDM filter design is proposed and experimentally demonstrated. The design has achieved flat-top transmission across all dies on a wafer, with a device footprint of 48*25 μm2, an insertion loss of 0.24 $\pm$ 0.18 dB, and a channel central wavelength standard deviation of 0.77 nm.