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. 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.

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 have many advantages in realizing such intelligent vision 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 5-bit 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 bP image sensor array can be scaled up to build a more complex vision-sensory neural network, which will find many promising applications for distributed and remote multispectral sensing.

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 µm, 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.

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 programable phase-change memory cells to perform four-element vector-vector dot multiplication. The GAN can generate a handwritten number ("7") in experiments and full 10 digits in simulation. We realize an optical random number generator, apply noise-aware training by injecting additional noise, and demonstrate the network's resilience to hardware nonidealities. Our results suggest the resilience and potential of more complex photonic generative networks based on large-scale, realistic photonic hardware.

A CAN-Bus Lightweight Authentication Scheme.

The design of the Controller Area Network (CAN bus) did not account for security issues and, consequently, attacks often use external mobile communication interfaces to conduct eavesdropping, replay, spoofing, and denial-of-service attacks on a CAN bus, posing a risk to driving safety. Numerous studies have proposed CAN bus safety improvement techniques that emphasize modifying the original CAN bus method of transmitting frames. These changes place additional computational burdens on electronic control units cause the CAN bus to lose the delay guarantee feature. Consequently, we proposed a method that solves these compatibility and security issues. Simple and efficient frame authentication algorithms were used to prevent spoofing and replay attacks. This method is compatible with both CAN bus and CAN-FD protocols and has a lower operand when compared with other methods.

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 on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge2Sb2Te5 during phase transition to control the waveguide spatial modes with a very high precision of up to 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 a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs.

On-the-fly closed-loop 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. 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 an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

Nonvolatile Electrically Reconfigurable Integrated Photonic Switch Enabled by a Silicon PIN Diode Heater.

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 large footprints and high energy consumption. As a promising alternative, chalcogenide phase-change materials (PCMs) exhibit strong optical modulation in a static, self-holding fashion, but the scalability of present PCM-integrated photonic applications is still limited by the poor optical or electrical actuation approaches. Here, with phase transitions actuated by in situ silicon PIN diode heaters, scalable nonvolatile electrically reconfigurable photonic switches using PCM-clad silicon waveguides and microring resonators are demonstrated. As a result, intrinsically compact and energy-efficient switching units operated with low driving voltages, near-zero additional loss, and reversible switching with high endurance are obtained in a complementary metal-oxide-semiconductor (CMOS)-compatible process. This work can potentially enable very large-scale CMOS-integrated programmable electronic-photonic systems such as optical neural networks and general-purpose integrated photonic processors.

Effects of Rhizome Integration on the Water Physiology of Phyllostachys edulis Clones Under Heterogeneous Water Stress.

Water is crucial to plant growth and development. Under heterogeneous environmental water deficiency, physiological integration of the rhizomatous clonal plant triggers a series of physiological cascades, which induces both signaling and physiological responses. It is known that the rhizome of Phyllostachys edulis, which connects associated clonal ramets, has important significance in this physiological integration. This significance is attributed to the sharing of water and nutrients in the vascular bundle of clonal ramets under heterogeneous water conditions. However, the physiological characteristics of physiological integration under heterogeneous water stress remain unclear. To investigate these physiological characteristics, particularly second messenger Ca2+ signaling characteristics, long-distance hormone signaling molecules, antioxidant enzyme activity, osmotic adjustment substance, and nitrogen metabolism, ramets with a connected (where integration was allowed to take place) and severed rhizome (with no integration) were compared in this study. The vascular bundle structure of the rhizome was also observed using laser confocal microscopy. Overall, the results suggest that interconnected rhizome of P. edulis can enhance its physiological function in response to drought-induced stress under heterogeneous water deficiency. These measured changes in physiological indices serve to improve the clonal ramets' drought adaptivity through the interconnected rhizome.

A study of lateral Schottky contacts in WSe2 and MoS2 field effect transistors using scanning photocurrent microscopy.

Schottky contacts, formed at metal/semiconductor interfaces, always have a large impact on the performance of field-effect transistors (FETs). Here, we report the experimental studies of Schottky contacts in two-dimensional (2D) transition metal dichalcogenide (TMDC) FET devices. We use scanning photocurrent microscopy (SPCM) to directly probe the spatial distribution of the in-plane lateral Schottky depletion regions at the metal/2D-TMDC interfaces. The laser incident position dependent and the gate voltage tunable polarity and magnitude of the short-circuit photocurrent reveal the existence of the in-plane Schottky depletion region laterally extending away from the metal contact edges along the channel. This lateral depletion region length is estimated to be around several microns and can be effectively tuned by the gate and drain-source biases. Our results solidify the importance of lateral Schottky depletion regions in the photoresponse of 2D TMDC optoelectronic devices.

Immunohistochemistry as a quick screening method for clinical detection of BRAF(V600E) mutation in melanoma patients.

With the increased uses of targeted therapeutics, diagnostic detection of target mutations becomes essential for the effective clinical applications of targeted therapeutics. Currently, there are two types of methods detecting target mutations in clinics: one is based on DNA sequence and the other uses the newly developed mutation-specific antibodies recognizing mutated proteins. Each method has its own advantages and disadvantages. Here, we explored the sensitivity and specificity of a new commercially available BRAF(V600E) mutation-specific mouse monoclonal antibody. Using routine manual immunohistochemistry (IHC), we tested tumor tissues from 38 melanoma patients. For those melanoma tissues with abundant endogenous melanin, we pretreated the tumor tissues with 3 % hydrogen peroxide to remove melanin for reliable signal detection. We also performed DNA sequencing and ARMS-PCR analyses for these 38 tumor samples. Comparing to the results from DNA-based detection methods, the IHC method with this BRAF(V600E) mutation-specific antibody displayed 100 % sensitivity and 92.9 % specificity. Hence, this IHC detection is sensitive for clinic uses as a simple, fast, inexpensive, and reliable method to screen cancer patients for the BRAF(V600E) mutation and could be easily adapted for use in most hospital pathology laboratories.