Reduced rank photonic computing accelerator

Use of artificial intelligence for tasks such as image classification and speech recognition has started to form an integral part of our lives. Facilitation of such tasks requires processing a huge amount of data, at times in real time, which has resulted in a computation bottleneck. Photonic cores promise ultra-fast convolutional processing by employing broadband optical links to perform parallelized matrix–vector multiplications (MVMs). Yet the scalability of photonic MVMs is limited by the footprint of the system and energy required for programming the weights, which scale with the matrix dimensionality (×). One approach is to reduce the number of hardware matrix weights required, which would allow for less aggressive scaling of the hardware. In this paper, we propose and experimentally demonstrate precisely such a hardware photonic architecture with reduced rank of operation, significantly improving on scalability and decreasing the system complexity. We employ the reduced photonic matrix with reconfigurable optical weights in image processing tasks where we demonstrate the ability to achieve edge detection and classification with 33% reduction in the conventional 3×3 kernel matrix and with no detectable loss of accuracy. While our demonstration is in photonics, this architecture can be universally adapted to MVM engines, and offers the potential for fast, scalable computations at a lower programming cost

Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks

To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot semantic segmentation methods first pre-train the models on `seen' classes, and then evaluate their generalization performance on `unseen' classes. However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes. To tackle these issues, we propose an efficient Training-free Few-shot 3D Segmentation netwrok, TFS3D, and a further training-based variant, TFS3D-T. Without any learnable parameters, TFS3D extracts dense representations by trigonometric positional encodings, and achieves comparable performance to previous training-based methods. Due to the elimination of pre-training, TFS3D can alleviate the domain gap issue and save a substantial amount of time. Building upon TFS3D, TFS3D-T only requires to train a lightweight query-support transferring attention (QUEST), which enhances the interaction between the few-shot query and support data. Experiments demonstrate TFS3D-T improves previous state-of-the-art methods by +6.93% and +17.96% mIoU respectively on S3DIS and ScanNet, while reducing the training time by -90%, indicating superior effectiveness and efficiency.Comment: Code is available at https://github.com/yangyangyang127/TFS3

Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement

The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its application to diverse downstream vision tasks. To improve its capacity on downstream tasks, few-shot learning has become a widely-adopted technique. However, existing methods either exhibit limited performance or suffer from excessive learnable parameters. In this paper, we propose APE, an Adaptive Prior rEfinement method for CLIP's pre-trained knowledge, which achieves superior accuracy with high computational efficiency. Via a prior refinement module, we analyze the inter-class disparity in the downstream data and decouple the domain-specific knowledge from the CLIP-extracted cache model. On top of that, we introduce two model variants, a training-free APE and a training-required APE-T. We explore the trilateral affinities between the test image, prior cache model, and textual representations, and only enable a lightweight category-residual module to be trained. For the average accuracy over 11 benchmarks, both APE and APE-T attain state-of-the-art and respectively outperform the second-best by +1.59% and +1.99% under 16 shots with x30 less learnable parameters.Comment: Code is available at https://github.com/yangyangyang127/AP

All optical tunable RF filter using elemental antimony

In the past decade, the proliferation of modern telecommunication technologies, including 5G, and the widespread adoption of the Internet-of-things (IoT) have led to an unprecedented surge in data generation and transmission. This surge has created an escalating demand for advanced signal processing capabilities. Microwave photonic (MWP) processors offer a promising solution to satisfy this unprecedented demand for data processing by capitalising on the high bandwidth and low latency achievable by optical systems. In this work, we introduce an integrated MWP processing unit for all-optical RF filtering using elemental antimony. We exploit the crystallisation dynamics of antimony to demonstrate a photonic leaky integrator, which is configured to operate as a first-order low-pass filter with a bandwidth of 300 kHz and ultra-compact footprint of 16 × 16 μm2. We experimentally demonstrate the implementation of such a filter as an envelope detector to demodulate an amplitude-modulated signal. Finally, a discussion on achieving bandwidth tunability is presented

Spatio-spectral control of coherent nanophotonics

Fast modulation of optical signals that carry multidimensional information in the form of wavelength, phase or polarization has fueled an explosion of interest in integrated photonics. This interest however masks a significant challenge which is that independent modulation of multi-wavelength carrier signals in a single waveguide is not trivial. Such challenge is attributed to the longitudinal direction of guided-mode propagation, limiting the spatial separation and modulation of electric-field. Here, we overcome this using a single photonic element that utilizes active coherent (near) perfect absorption. We make use of standing wave patterns to exploit the spatial-degrees-of-freedom of in-plane modes and individually address elements according to their mode number. By combining the concept of coherent absorption in spatio-spectral domain with active phase-change nanoantennas, we engineer and test an integrated, reconfigurable and multi-spectral modulator operating within a single element. Our approach demonstrates for the first time, a non-volatile, wavelength-addressable element, providing a pathway for exploring the tunable capabilities in both spatial and spectral domains of coherent nanophotonics

In-memory photonic dot-product engine with electrically programmable weight banks

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic–electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic–electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ/dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (≥87.36) that leads to an enhanced computing accuracy (standard deviation σ ≤ 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%

Ultra-high strength metal matrix composites (MMCs) with extended ductility manufactured by size-controlled powder and spherical cast tungsten carbide

The main challenge of particle reinforced metal matrix composites (MMCs) is balancing strength and ductility. This research uses type 420 stainless steel and spherical cast tungsten carbide (WC/W2C) with a similar powder size and range as raw powders to manufacture laser powder bed fusion (LPBF) 420 + 5 wt% WC/W2C MMCs. LPBF 420 + 5 wt% WC/W2C MMCs contain austenite, martensite, and W-rich carbides (WC/W2C, FeW3C, M6C, and M7C3) from nanometre to micrometre scale. The well-balanced composition creates a crack-free reaction layer between the reinforced particles and matrix. This reaction layer consists of two distinct layers, depending on the element concentration. The LPBF 420 + 5 wt% WC/W2C MMCs achieved an excellent compressive strength of ∼5.5 GPa and a considerable fracture strain exceeding 50 %. The underlying mechanisms for the improved mechanical properties are discussed, providing further insight to advance the application of MMCs via additive manufacturing

Impact of the Hole Transport Layer on the Charge Extraction of Ruddlesden-Popper Perovskite Solar Cells

Recent works demonstrate that polyelectrolytes as a hole transport layer (HTL) offers superior performance in Ruddlesden-Popper perovskite solar cells (RPPSCs) compared to poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). The factors contributing to such improvement need to be systematically investigated. To achieve this, we have systematically investigated how the two HTLs affect the morphology, crystallinity, and orientation of the Ruddlesden-Popper perovskite (RPP) films as well as the charge extraction of the RPPSCs. PEDOT:PSS as a HTL leads to RPP films of low crystallinity and with a number of large pinholes. These factors lead to poor charge carrier extraction and significant charge recombination in the RPPSCs. Conversely, a PCP-Na HTL gives rise to highly crystalline and pinhole-free RPPSC films. Moreover, a PCP-Na HTL provides a better energy alignment at the perovskite/HTL interface because of its higher work function compared to PEDOT:PSS. Consequently, devices using PCP-Na as HTLs are more efficient in extracting charge carriers

Scalable high-precision trimming of photonic resonances by polymer exposure to energetic beams

Integrated photonic circuits (PICs) have seen an explosion in interest, through to commercialization in the past decade. Most PICs rely on sharp resonances to modulate, steer, and multiplex signals. However, the spectral characteristics of high-quality resonances are highly sensitive to small variations in fabrication and material constants, which limits their applicability. Active tuning mechanisms are commonly employed to account for such deviations, consuming energy and occupying valuable chip real estate. Readily employable, accurate, and highly scalable mechanisms to tailor the modal properties of photonic integrated circuits are urgently required. Here, we present an elegant and powerful solution to achieve this in a scalable manner during the semiconductor fabrication process using existing lithography tools: by exploiting the volume shrinkage exhibited by certain polymers to permanently modulate the waveguide’s effective index. This technique enables broadband and lossless tuning with immediate applicability in wide-ranging applications in optical computing, telecommunications, and free-space optics

Tuning the Energetic Landscape of Ruddlesden-Popper Perovskite Films for Efficient Solar Cells

Ruddlesden-Popper perovskite films deposited with different methods show very diverse phase segregation and composition. When DMSO is used as solvent, the conventional method based on spin-coating and annealing produces very poor devices, whereas the vacuum-assisted method proposed here allows obtaining devices with efficiency up to 14.14%. The conventional method gives rise to a three-dimensional (3D)-like phase on the top of the film but dominant n = 2 phase with large domains (∼40 μm) at the bottom of the film. These n = 2 domains are oriented with their inorganic slabs parallel to the substrate and inhibit the charge transport in the vertical direction. Consequently, severe monomolecular and bimolecular charge recombination occurs in the solar cells. Conversely, the vacuum-assisted method yields films with a 3D-like phase dominant throughout their entire thickness and only a small amount of n ≤ 2 domains of limited dimensions (∼3 μm) at the bottom, which facilitate charge transport and reduce charge recombination.</p