VCSEL with finite-size high-contrast metastructure

© Copyright 2018 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.High-contrast metastructures like one-dimensional high-contrast gratings (HCGs) are promising to improve the performance of conventional VCSELs, also presenting a basis for new applications. Different from the previous studies where HCGs are always modelled being of infinite size, we studied here the finite-size HCGs, which match the real situation. We observe finite-size HCGs behaving very differently from infinite-size HCGs. The reflectivity of a finitesize HCG strongly depends on the HCG size and the source size. At the same time, the simulation results show, that finite-size HCGs can shape the output beam, and a Gaussian-like reflected wave is typically achieved. Most important the normally incident light is partly redirected to the in-plane direction, showing unidirectional transmission. Monolithically integrated HCG-based optical sensors can be based on this novel effect. An integrable HCG reflector was fabricated with GaInP as the sacrificial layer targeting the application of HCG-VCSEL at 980 nm range. The measured reflectivity agrees well with the calculated reflectivity

Active beam steering enabled by photonic crystal surface emitting laser

Emitting light towards on-demand directions is important for various optoelectronic applications, such as optical communication, displaying, and ranging. However, almost all existing directional emitters are assemblies of passive optical antennae and external light sources, which are usually bulky, fragile, and with unendurable loss of light power. Here we theoretically propose and experimentally demonstrate a new conceptual design of directional emitter, by using a single surface-emitting laser source itself to achieve dynamically controlled beam steering. The laser is built on photonic crystals that operates near the band edges in the continuum. By shrinking laser sizes into tens-of-wavelength, the optical modes quantize in three-dimensional momentum space, and each of them directionally radiates towards the far-field. Further utilizing the luminescence spectrum shifting effect under current injection, we consecutively select a sequence of modes into lasing action and show the laser maintaining in single mode operation with linewidths at a minimum of $1.8$ MHz and emitting power of $\sim$ ten milliwatts, and we demonstrate fast beam steering across a range of $3.2^\circ \times 4^\circ$ in a time scale of $500$ nanoseconds. Our work proposes a novel method for on-chip active beam steering, which could pave the way for the development of automotive, industrial, and robotic applications.Comment: 23 pages, 5 figure

OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression

This paper presents a language-powered paradigm for ordinal regression. Existing methods usually treat each rank as a category and employ a set of weights to learn these concepts. These methods are easy to overfit and usually attain unsatisfactory performance as the learned concepts are mainly derived from the training set. Recent large pre-trained vision-language models like CLIP have shown impressive performance on various visual tasks. In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space. Specifically, we reformulate this task as an image-language matching problem with a contrastive objective, which regards labels as text and obtains a language prototype from a text encoder for each rank. While prompt engineering for CLIP is extremely time-consuming, we propose OrdinalCLIP, a differentiable prompting method for adapting CLIP for ordinal regression. OrdinalCLIP consists of learnable context tokens and learnable rank embeddings; The learnable rank embeddings are constructed by explicitly modeling numerical continuity, resulting in well-ordered, compact language prototypes in the CLIP space. Once learned, we can only save the language prototypes and discard the huge language model, resulting in zero additional computational overhead compared with the linear head counterpart. Experimental results show that our paradigm achieves competitive performance in general ordinal regression tasks, and gains improvements in few-shot and distribution shift settings for age estimation. The code is available at https://github.com/xk-huang/OrdinalCLIP.Comment: Accepted by NeurIPS2022. Code is available at https://github.com/xk-huang/OrdinalCLI

DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation

Talking head synthesis is a promising approach for the video production industry. Recently, a lot of effort has been devoted in this research area to improve the generation quality or enhance the model generalization. However, there are few works able to address both issues simultaneously, which is essential for practical applications. To this end, in this paper, we turn attention to the emerging powerful Latent Diffusion Models, and model the Talking head generation as an audio-driven temporally coherent denoising process (DiffTalk). More specifically, instead of employing audio signals as the single driving factor, we investigate the control mechanism of the talking face, and incorporate reference face images and landmarks as conditions for personality-aware generalized synthesis. In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without any further fine-tuning. Additionally, our DiffTalk can be gracefully tailored for higher-resolution synthesis with negligible extra computational cost. Extensive experiments show that the proposed DiffTalk efficiently synthesizes high-fidelity audio-driven talking head videos for generalized novel identities. For more video results, please refer to \url{https://sstzal.github.io/DiffTalk/}.Comment: Project page https://sstzal.github.io/DiffTalk

Surface passivation of random alloy AlGaAsSb avalanche photodiode

AlGaAsSb attracts significant interest for near‐infrared avalanche photodiodes (APD). The authors report a two‐order reduction in the dark current and a six‐time enhancement of gain in random alloy (RA) AlGaAsSb APD that is surface passivated by conformal coating of Al2O3 via atomic layer deposition (ALD). The dark currents of the APDs with 400‐µm diameter (dry etched) at 90% breakdown voltage (0.9 Vbr) are (5.5 ± 0.5) × 10−5 A, (2.1 ± 0.4) × 10−5 A, and (6.2 ± 0.8) × 10−7 A for non‐passivated, Si3N4 passivated, and Al2O3 passivated devices, respectively. The dark current at a gain of 10 for the Al2O3 passivated device is 1 × 10−8 A which is comparable to the reported value for 100‐µm diameter mesa diodes passivated by SU‐8. Maximum gain values of 6, 12, and 35 were obtained for non‐passivated, Si3N4 passivated, and Al2O3 passivated devices, respectively. Moreover, punch‐through capacitance of 8 pF in a spectral response of 450 to 850 nm was obtained. Thus, Al2O3 passivation can be the best solution for antimonide optoelectronic devices

Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-P\'erot laser with saturable absorber

Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.Comment: 10 pages, 8 figure

Integrative epigenome profiling of 47XXY provides insights into whole genomic DNA hypermethylation and active chromatin accessibility

Klinefelter syndrome (KS, 47XXY) is a disorder characterized by sex chromosomal aneuploidy, which may lead to changes in epigenetic regulations of gene expression. To define epigenetic architectures in 47XXY, we annotated DNA methylation in euploid males (46XY) and females (46XX), and 47XXY individuals using whole genome bisulfite sequencing (WGBS) and integrated chromatin accessbilty, and detected abnormal hypermethylation in 47XXY. Furthermore, we detected altered chromatin accessibility in 47XXY, in particular in chromosome X, using Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) in cultured amniotic cells. Our results construct the whole genome-wide DNA methylation map in 47XXY, and provide new insights into the early epigenomic dysregulation resulting from an extra chromosome X in 47XXY