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 Ge
Sb
Te
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.
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.
Nonvolatile electrically reconfigurable photonic switches based on phase‐change‐material‐clad silicon waveguides and microring resonators are demonstrated via in situ silicon PIN diode heaters. Low‐energy, compact, low‐loss, low‐voltage, and high‐cyclability operations at moderate speeds are obtained in a complementary metal‐oxide‐semiconductor‐compatible process, promising very large‐scale programmable electronic–photonic systems such as optical neural networks and general‐purpose integrated photonic processors.
Abstract
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.
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 WSe
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.
Neural networks have been widely used for advanced tasks from image recognition to natural language processing. Many recent works focus on improving the efficiency of executing neural networks in ...diverse applications. Researchers have advocated processing‐in‐memory (PIM) architecture as a promising candidate for training and testing neural networks because PIM design can reduce the communication cost between storage and computing units. However, there exist noises in the PIM system generated from the intrinsic physical properties of both memory devices and the peripheral circuits. The noises introduce challenges in stably training the systems and achieving high test performance, e.g., accuracy in classification tasks. This review discusses the current approaches to tolerating noise effects for both training and inference in PIM systems and provides an analysis from a hardware–software codesign perspective. Noise‐tolerant strategies for PIM systems based on resistive random‐access memory (ReRAM), including circuit‐level, algorithm‐level, and system‐level solutions are explained. In addition, we also present some selected noise‐tolerate cases in PIM systems for generative adversarial networks and physical neural networks.
This review mainly discusses the current approaches to tolerate noise effects for both training and inference in processing‐in‐memory (PIM) systems and provides analysis from a hardware–software codesign perspective. Noise‐tolerant strategies for resistive random‐access memory (ReRAM)‐based systems are explained, including the circuit‐level, algorithm‐level, and system‐level solutions.
Among layered and 2D semiconductors, there are many with substantial optical anisotropy within individual layers, including group‐IV monochalcogenides MX (M = Ge or Sn and X = S or Se) and black ...phosphorous (bP). Recent work has suggested that the in‐plane crystal orientation in such materials can be switched (e.g., rotated through 90°) through an ultrafast, displacive (i.e., nondiffusive), nonthermal, and lower‐power mechanism by strong electric fields, due to in‐plane dielectric anisotropy. In theory, this represents a new mechanism for light‐controlling‐light in photonic integrated circuits (PICs). Herein, numerical device modeling is used to study device concepts based on switching the crystal orientation of SnSe and bP in PICs. Ring resonators and 1 × 2 switches with resonant conditions that change with the in‐plane crystal orientations SnSe and bP are simulated. The results are broadly applicable to 2D materials with ferroelectric and ferroelastic crystal structures including SnO, GeS, and GeSe.
Among layered and 2D semiconductors, there are many with substantial optical anisotropy within individual layers. Herein, numerical modeling is used to study device concepts based on switching ferroelastic domain patterns in triaxial SnSe and black phosphorous. The results are broadly applicable to using 2D materials with ferroelectric and ferroelastic crystal structures in photonic integrated circuits.
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
, 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 Ca
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
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.
The Bayesian neural network (BNN) combines the strengths of neural networks and statistical modeling in that it simultaneously performs posterior predictions and quantifies the uncertainty of the ...predictions. Integrated photonics has emerged as a promising hardware platform of neural network accelerators capable of energy-efficient, low latency, and parallel computing. However, photonic neural networks demonstrated to date are mostly deterministic network models. Here, we extend the photonic neural network to a statistical model and proposed a photonic Bayesian neural network (P-BNN) architecture based on the integrated photonic platform and harnessing the inherent optical noises. The Bayesian neuron is realized by controlling the probability distribution of the signal-amplified spontaneous emission (signal-ASE) beat noise. We show the P-BNN's advantages in making predictions using the posterior distribution by simulating a p-BNN to perform handwritten number classification tasks. The simulation results show that the proposed P-BNN not only makes successful predictions on the expected images from the test dataset but also detects and rejects the unexpected images outside the training datasets. The P-BNN architecture is compatible with on-chip optical amplifiers and can be scaled up using current and emerging integrated photonics technologies, thus is promising for practical neural network applications.
The programmability in integrated photonic systems fosters advancements across diverse technologies, from data centers to optical neural networks and quantum information processing. Phase-change ...materials (PCMs) can offer an ideal solution thanks to their reversible switching, large index contrast, and non-volatile behavior, enabling programmability with no static power consumption. In this thesis, I will mainly introduce several phase-change photonic devices that can contribute to various photonic applications such as optical computing, signal processing, and optical communications.First, 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. 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 for large-scale photonic neural networks with ultrahigh computation throughputs.Then we demonstrate a photonic generative network as a part of a generative adversarial network (GAN) that can generate a handwritten number in experiments. 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.Finally, we report direct-write and rewritable photonic circuits based on a low-loss phase change material (PCM) thin film, in which complete end-to-end functional photonic circuits can be created by direct laser writing in one step without additional fabrication processes. The direct-write phase-change photonic circuit affords exceptional flexibility, allowing any part of the circuit to be erased and rewritten, facilitating rapid design modification and reprogramming. We demonstrate the versatility of this technique with various photonic circuits for diverse applications, including an optical interconnect fabric for reconfigurable networking, a photonic crossbar array as a tensor core for optical computing, and a tunable optical filter for optical signal processing.
Integration of phase change material (PCM) with photonic integrated circuits can transform large-scale photonic systems by providing non-volatile control over phase and amplitude. The next generation ...of commercial silicon photonic processes can benefit from the addition of PCM to enable ultra-low power, highly reconfigurable, and compact photonic integrated circuits for large-scale applications. Despite all the advantages of PCM-based photonics, today’s commercial foundries do not provide them in their silicon photonic processes yet. We demonstrate the first-ever electrically programmable PCM device that is monolithically post-processed in a commercial foundry silicon photonics process using a few fabrication steps and coarse-resolution photolithography. These devices achieved 1.4 dB/μm of amplitude switching contrast using a thin layer of 12.5 nm GeSbTe in this work. We have also characterized the reconfiguration speed as well as repeatability of these devices over 20,000 switching cycles. Our solution enables non-volatile photonic VLSI systems that can be fabricated at low cost and high reliability in a commercial foundry process, paving the way for the development of non-volatile programmable photonic integrated circuits for a variety of emerging applications.
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