arXiv subjects

Changming Wu

Publications and source records attributed to Changming Wu.

10 recordsLinked to original sources

arXiv2024

Reconfigurable inverse designed phase-change photonics

Chalcogenide phase-change materials (PCMs) offer a promising approach to programmable photonics thanks to their nonvolatile, reversible phase transitions and high refractive index contrast. However, conventional designs are limited by global phase control over entire PCM thin films between fully amorphous and fully crystalline states, which restricts device functionality and confines design flexibility and programmability. In this work, we present a novel approach that leverages pixel-level control of PCM in inverse-designed photonic devices, enabling highly reconfigurable, multi-functional operations. We integrate low-loss Sb2Se3 onto a multi-mode interferometer (MMI) and achieve precise, localized phase manipulation through direct laser writing. This technique allows for flexible programming of the photonic device by adjusting the PCM phase pattern rather than relying on global phase states, thereby enhancing device adaptability. As a proof of concept, we programmed the device as a wavelength-division multiplexer and subsequently reconfigured it into a mode-division multiplexer. Our results underscore the potential of combining inverse design with pixel-wise tuning for next-generation programmable phase-change photonic systems.

physics.optics↗

arXiv2023

Freeform Direct-write and Rewritable Photonic Integrated Circuits in Phase-Change Thin Films

Photonic integrated circuits (PICs) with rapid prototyping and reprogramming capabilities promise revolutionary impacts on a plethora of photonic technologies. Here, 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.

physics.optics↗

arXiv2022

Long-range transport of 2D excitons with acoustic waves

Excitons are elementary optical excitation in semiconductors. The ability to manipulate and transport these quasiparticles would enable excitonic circuits and devices for quantum photonic technologies. Recently, interlayer excitons in 2D semiconductors have emerged as a promising candidate for engineering excitonic devices due to their long lifetime, large exciton binding energy, and gate tunability. However, the charge-neutral nature of the excitons leads to weak response to the in-plane electric field and thus inhibits transport beyond the diffusion length. Here, we demonstrate the directional transport of interlayer excitons in bilayer WSe2 driven by the propagating potential traps induced by surface acoustic waves (SAW). We show that at 100 K, the SAW-driven excitonic transport is activated above a threshold acoustic power and reaches 20 mm, a distance at least ten times longer than the diffusion length and only limited by the device size. Temperature-dependent measurement reveals the transition from the diffusion-limited regime at low temperature to the acoustic field-driven regime at elevated temperature. Our work shows that acoustic waves are an effective, contact-free means to control exciton dynamics and transport, promising for realizing 2D materials-based excitonic devices such as exciton transistors, switches, and transducers up to room temperature.

cond-mat.mes-hall↗

arXiv2021

Harnessing Optoelectronic Noises in a Photonic Generative Network

Integrated optoelectronics is emerging as a promising platform of neural network accelerator, which affords efficient in-memory computing and high bandwidth interconnectivity. The inherent optoelectronic noises, however, make the photonic systems error-prone in practice. It is thus imperative to devise strategies to mitigate and, if possible, harness noises in photonic computing systems. Here, we demonstrate a photonic generative network as a part of a generative adversarial network (GAN). This network is implemented with a photonic core consisting of an array of four programable phase-change memory cells to perform 4-elements vector-vector dot multiplication. We demonstrate that the GAN can generate a handwritten number ("7") in experiments and full ten digits in simulation. We realize an optical random number generator derived from the amplified spontaneous emission noise, apply noise-aware training by injecting additional noise and demonstrate the network's resilience to hardware non-idealities. Our results suggest the resilience and potential of more complex photonic generative networks based on large-scale, realistic photonic hardware.

cs.ET↗

arXiv2021

Programmable black phosphorus image sensor for broadband optoelectronic edge computing

Image sensors with internal computing capability enable in-sensor computing that can significantly reduce the communication latency and power consumption for machine vision in distributed systems and robotics. Two-dimensional semiconductors are uniquely advantageous in realizing such intelligent visionary sensors because of their tunable electrical and optical properties and amenability for heterogeneous integration. Here, we report a multifunctional infrared image sensor based on an array of black phosphorous programmable phototransistors (bP-PPT). By controlling the stored charges in the gate dielectric layers electrically and optically, the bP-PPT's electrical conductance and photoresponsivity can be locally or remotely programmed with high precision to implement an in-sensor convolutional neural network (CNN). The sensor array can receive optical images transmitted over a broad spectral range in the infrared and perform inference computation to process and recognize the images with 92% accuracy. The demonstrated multispectral infrared imaging and in-sensor computing with the black phosphorous optoelectronic sensor array can be scaled up to build a more complex visionary neural network, which will find many promising applications for distributed and remote multispectral sensing.

physics.app-ph↗

arXiv2020

On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning

Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1]. In this work we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate autonomous research methodology (i.e. autonomous hypothesis definition and evaluation) that can place complex, advanced materials in reach, allowing scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. Additionally, this robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. We used the real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) at the synchrotron beamline to accelerate the fundamentally interconnected tasks of rapid phase mapping and property optimization, with each cycle taking seconds to minutes, resulting in the discovery of a novel epitaxial nanocomposite phase-change memory material.

cond-mat.mtrl-sci↗

arXiv2020

Refractive uses of layered and two-dimensional materials for integrated photonics

The scientific community has witnessed tremendous expansion of research on layered (i.e. two-dimensional, 2D) materials, with increasing recent focus on applications to photonics. Layered materials are particularly exciting for manipulating light in the confined geometry of photonic integrated circuits, where key material properties include strong and controllable light-matter interaction, and limited optical loss. Layered materials feature tunable optical properties, phases that are promising for electro-optics, and a panoply of polymorphs that suggest a rich design space for highly-nonperturbative photonic integrated devices based on phase-change functionality. All of these features are manifest in materials with band gap above the photonics-relevant near-infrared (NIR) spectral band ($\sim$ 0.5 - 1 eV), meaning that they can be harnessed in refractive (i.e. non-absorptive) applications.

cond-mat.mtrl-sci↗

arXiv2020

Programmable Phase-change Metasurfaces on Waveguides for Multimode Photonic Convolutional Neural Network

Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics, for machine learning algorithms such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on metasurface made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge-Sb-Te during phase transition to control the waveguide spatial modes with a very high precision of up 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate an optical convolutional neural network that can perform image processing and classification tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is very promising toward a large-scale photonic processor for high-throughput optical neural networks.

physics.optics↗

arXiv2019

Nonvolatile electrically reconfigurable integrated photonic switch

Reconfigurability of photonic integrated circuits (PICs) has become increasingly important due to the growing demands for electronic-photonic systems on a chip driven by emerging applications, including neuromorphic computing, quantum information, and microwave photonics. Success in these fields usually requires highly scalable photonic switching units as essential building blocks. Current photonic switches, however, mainly rely on materials with weak, volatile thermo-optic or electro-optic modulation effects, resulting in a large footprint and high energy consumption. As a promising alternative, chalcogenide phase-change materials (PCMs) exhibit strong modulation in a static, self-holding fashion. Here, we demonstrate nonvolatile electrically reconfigurable photonic switches using PCM-clad silicon waveguides and microring resonators that are intrinsically compact and energy-efficient. With phase transitions actuated by in-situ silicon PIN heaters, near-zero additional loss and reversible switching with high endurance are obtained in a complementary metal-oxide-semiconductor (CMOS)-compatible process. Our work can potentially enable very large-scale general-purpose programmable integrated photonic processors.

physics.app-ph↗

arXiv2016

The origin of bias independent conductance plateaus and zero bias conductance peaks in Bi2Se3/NbSe2 hybrid structures

Superconducting proximity effect (SPE) in topological insulator (TI) and superconductor (SC) hybrid structure has attracted intense attention in recent years in an effort to search for mysterious Majorana fermions (MFs) in condensed matter systems. Here we report on the SPE in a Bi2Se3/NbSe2 junction fabricated with an all-dry transfer method. Resulting from the highly transparent interface, two sharp resistance drops are observed at 7 K and 2 K, respectively, corresponding to the superconducting transition of NbSe2 flake and the SPE induced superconductivity in Bi2Se3 flake. Experimentally measured differential conductance spectra exhibit a bias-independent conductance plateau (BICP) in the vicinity of zero bias below 7 K. As temperatures further decrease a zero bias conductance peak (ZBCP) emerges from the plateau and becomes more enhanced and sharpened at lower temperatures. Our numerically simulated differential conductance spectra reproduce the observed BICP and ZBCP and show that the SPE in topological surface states (TSS) is much stronger than that in the bulk states of Bi2Se3. The SPE induced superconducting gap for the TSS of Bi2Se3 is comparable to that of NbSe2 and gives rise to the observed BICP below 7 K. In contrast, the SPE induced superconducting gap for the bulk states of Bi2Se3 is an order of magnitude smaller than that of NbSe2 and superconducting TSS. These weakly paired bulk states in Bi2Se3 give rise to the ZBCP below 2 K. Our study has clearly unveiled the different roles of TSS and bulk stats in SPE, clarified the physical origin of the SPE induced features, and shined light on further investigation of SPE and MF in TI/SC hybrid structures.

cond-mat.supr-con↗