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Abstract Nonvolatile photonic integrated circuits employing phase change materials have relied either on optical switching mechanisms with precise multi-level control but poor scalability or electrical switching with seamless integration and scalability but mostly limited to a binary response. Recent works have demonstrated electrical multi-level switching; however, they relied on the stochastic nucleation process to achieve partial crystallization with low demonstrated repeatability and cyclability. Here, we re-engineer waveguide-integrated microheaters to achieve precise spatial control of the temperature profile (i.e., hotspot) and, thus, switch deterministic areas of an embedded phase change material cell. We experimentally demonstrate this concept using a variety of foundry-processed doped-silicon microheaters on a silicon-on-insulator platform to trigger multi-step amorphization and reversible switching of Sb₂Se₃and Ge₂Sb₂Se₄Te alloys. We further characterize the response of our microheaters using Transient Thermoreflectance Imaging. Our approach combines the deterministic control resulting from a spatially resolved glassy-crystalline distribution with the scalability of electro-thermal switching devices, thus paving the way to reliable multi-level switching towards robust reprogrammable phase-change photonic devices for analog processing and computing. 

Acoustically driven ferromagnetic resonance (ADFMR) is a platform that enables efficient generation and detection of spin waves via magnetoelastic coupling with surface acoustic waves (SAWs). While previous studies successfully achieved ADFMR in ferromagnetic metals, there are only few reports on ADFMR in magnetic insulators such as yttrium iron garnet (Y3Fe5O12, YIG) despite more favorable spin wave properties, including low damping and long coherence length. The growth of high-quality YIG films for ADFMR devices is a major challenge due to poor lattice-matching and thermal degradation of the piezoelectric substrates during film crystallization. In this work, we demonstrate ADFMR of YIG thin films on LiNbO3 (LNO) substrates. We employed a SiOx buffer layer and rapid thermal annealing for crystallization of YIG films with minimal thermal degradation of LNO substrates. Optimized ADFMR device designs and time-gating measurements were used to enhance the ADFMR signal and overcome the intrinsically low magnetoelastic coupling of YIG. YIG films have a polycrystalline structure with an in-plane easy direction due to biaxial stresses induced during cooling after crystallization. The YIG device shows clear ADFMR patterns with maximum absorption for H ≈ 160 mT parallel to SAW propagation, which is consistent with our simulation results based on existing theoretical models. These results expand possibilities for developing efficient spin wave devices with magnetic insulators. 

Photonic integrated circuits (PICs) with rapid prototyping and reprogramming capabilities promise revolutionary impacts on a plethora of photonic technologies. We report direct-write and rewritable photonic circuits on a low-loss phase-change material (PCM) thin film. Complete end-to-end PICs are directly laser-written in one step without additional fabrication processes, and any part of the circuit can be erased and rewritten, facilitating rapid design modification. We demonstrate the versatility of this technique for diverse applications, including an optical interconnect fabric for reconfigurable networking, a photonic crossbar array for optical computing, and a tunable optical filter for optical signal processing. By combining the programmability of the direct laser writing technique with PCM, our technique unlocks opportunities for programmable photonic networking, computing, and signal processing. Moreover, the rewritable photonic circuits enable rapid prototyping and testing in a convenient and cost-efficient manner, eliminate the need for nanofabrication facilities, and thus promote the proliferation of photonics research and education to a broader community. 

A photonic generative adversarial network that harnesses optoelectronic noises to generate handwritten numbers is demonstrated. 

Abstract Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.