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Virtual Special Issue Dedicated to the 10th International conference on materials for Advanced Technologies (ICMAT), Symposium C: Semiconductor photonics

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IEEE JOURNAL OF QUANTUM ELECTRONICS 2020年第2期56卷 1-3页

作者： Sweeney, Stephen John Fan, Weijun Ooi, Boon S. Zhang, Dao Hua Univ Surrey Adv Technol Inst Guildford GU2 7XH Surrey England Univ Surrey Dept Phys Guildford GU2 7XH Surrey England Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore King Abdullah Univ Sci & Technol Elect & Math Sci & Engn Div Thuwal 23955 Saudi Arabia

This Special Issue is associated with the 10th International conference on materials for Advanced Technologies (ICMAT), Symposium C: Semiconductor photonics held on 23–28 June, 2019 at Marina Bay Sands in Singapore. Organised through the materials Research Society of Singapore (MRS-S), ICMAT has been held since 2001 and has attracted strong interest amongst the global materials science and engineering research communities. The 10th conference in the ICMAT series attracted over 3,500 delegates with a number of keynote and plenary presentations notably including four Nobel Laureates in Physics and Chemistry. The ICMAT conference consists of a number of topical symposia. Symposium C was organized jointly with Australian MRS and co-chaired by Professors Dao-Hua Zhang (NTU, Singapore), Chennupati Jagadish (ANU, Australia), Stephen J. Sweeney (University of Surrey, UK), Boon S. Ooi (KAUST, Saudi Arabia), Hao Gong (NUS, Singapore) and Weijun Fan (NTU, Singapore). The Symposium focused on recent developments in materials for applications in photonic devices covering systems including III-V and II-VI semiconductors, group IV alloys for silicon compatibility, oxides and perovskites for solar cell and emitter applications, quantum dot based devices and in general a wide variety of organic and inorganic materials spanning applications from the ultra-violet through to the mid-infrared. In total, 20 invited and 53 oral and 19 poster papers were presented over the four days of technical sessions. [ABSTRACT from AUTHOR]

关键词： Special issues and sections Meetings photonics III-V semiconductor materials II-VI semiconductor materials Perovskites Plasmons Photodetectors Quantum dots

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photonic convolutional accelerator and neural network in the Tera-OPs regime based on Kerr microcombs 25

Photonic convolutional accelerator and neural network in the...

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conference on Integrated Optics - devices, materials, and Technologies XXV

作者： Xu, Xingyuan Tan, Mengxi Corcoran, Bill Wu, Jiayang Boes, Andreas Nguyen, Thach G. Chu, Sai T. Little, Brent E. Hicks, Damien G. Morandotti, Roberto Mitchell, Arnan Moss, David J. Swinburne Univ Technol Opt Sci Ctr Hawthorn Vic 3122 Australia Monash Univ Dept Elect & Comp Syst Engn Clayton Vic 3800 Australia RMIT Univ Sch Engn Melbourne Vic 3001 Australia City Univ Hong Kong Dept Phys Tat Chee Ave Hong Kong Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Precis Mech Xian Peoples R China Walter & Eliza Hall Inst Med Res Bioinformat Div Parkville Vic 3052 Australia INRS Energie Mat & Telecommun 1650 Blvd Lionel Boulet Varennes PQ J3X 1S2 Canada Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China

ISBN: (纸本)9781510642140

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-OPS (TOPS - operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously - enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.

关键词： Optical neural networks neuromorphic processor microcomb convolutional accelerator

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Opportunities for Cross-Layer Design in High-Performance computing systems with Integrated Silicon photonic Networks

Opportunities for Cross-Layer Design in High-Performance Com...

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Mirza, Asif Avari, Shadi Manafi Taheri, Ebadollah Pasricha, Sudeep Nikdast, Mahdi Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA

ISBN: (纸本)9783981926347

With the ever growing complexity of high-performance computing (HPC) systems to satisfy emerging application requirements (e.g., high memory bandwidth requirement for machine learning applications), the performance bottleneck in such systems has moved from being computation-centric to be more communication-centric. Silicon photonic interconnection networks have been proposed to address the aggressive communication requirements in HPC systems, to realize higher bandwidth, lower latency, and better energy efficiency. There have been many successful efforts on developing silicon photonic devices, integrated circuits, and architectures for HPC systems. Moreover, many efforts have been made to address and mitigate the impact of different challenges (e.g., fabrication process and thermal variations) in silicon photonic interconnects. However, most of these efforts have focused only on a single design layer in the system design space (e.g., device, circuit or architecture level). Therefore, there is often a gap between what a design technique can improve in one layer, and what it might impair in another one. In this paper, we discuss the promise of cross-layer design methodologies for HPC systems integrating silicon photonic interconnects. In particular, we discuss how such cross-layer design solutions based on cooperatively designing and exchanging design objectives among different system design layers can help achieve the best possible performance when integrating silicon photonics into HPC systems.

关键词： High-performance computing silicon photonics cross-layer design

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Roadmap for phase change materials in photonics and beyond (vol 26, 107946, 2023)

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ISCIENCE 2023年第12期26卷 107946页

作者： Prabhathan, Patinharekandy Sreekanth, Kandammathe Valiyaveedu Teng, Jinghua Ko, Joo Hwan Yoo, Young Jin Jeong, Hyeon-Ho Lee, Yubin Zhang, Shoujun Cao, Tun Popescu, Cosmin-Constantin Mills, Brian Gu, Tian Fang, Zhuoran Chen, Rui Tong, Hao Wang, Yi He, Qiang Lu, Yitao Liu, Zhiyuan Yu, Han Mandal, Avik Cui, Yihao Ansari, Abbas Sheikh Bhingardive, Viraj Kang, Myungkoo Lai, Choon Kong Merklein, Moritz Mueller, Maximilian J. Song, Young Min Tian, Zhen Hu, Juejun Losurdo, Maria Majumdar, Arka Miao, Xiangshui Chen, Xiao Gholipour, Behrad Richardson, Kathleen A. Eggleton, Benjamin J. Sharda, Kanudha Wuttig, Matthias Singh, Ranjan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore 637371 Singapore Centre for Disruptive Photonic Technologies The Photonic Institute 50 Nanyang Avenue Singapore 639798 Singapore Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A∗STAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Republic of Singapore School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju 61005 Republic of Korea School of Materials Science and Engineering Gwangju Institute of Science and Technology Gwangju 61005 Republic of Korea DELL Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering Key Laboratory of Optoelectronic Information Technology (Ministry of Education of China) Tianjin University Tianjin 300072 China DELL School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian 116024 China Department of Materials Science &amp Engineering Massachusetts Institute of Technology Cambridge MA USA Materials Research Laboratory Massachusetts Institute of Technology Cambridge MA USA Department of Electrical &amp Computer Engineering University of Washington Washington Seattle USA Wuhan National Research Center for Optoelectronics School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China Institute of Advanced Materials Beijing Normal University Beijing 100875 China Nanoscale Optics Lab ECE Department University of Alberta Edmonton Canada CREOL College of Optics and Photonics University of Central Florida Orlando FL USA Institute of Photonics and Optical Science (IPOS) School of Physics The University of Sydney New South Wales NSW 2006 Australia The University of Sydney Nano Institute (Sydney Nano) The University of Sydney New South Wales NSW 2006 Australia Insti

Phase Change materials (PCMs) have demonstrated tremendous potential as a platform for achieving diverse functionalities in active and reconfigurable micro-nanophotonic devices across the electromagnetic spectrum, ranging from terahertz to visible frequencies. This comprehensive roadmap reviews the material and device aspects of PCMs, and their diverse applications in active and reconfigurable micro-nanophotonic devices across the electromagnetic spectrum. It discusses various device configurations and optimization techniques, including deep learning-based metasurface design. The integration of PCMs with photonic Integrated Circuits and advanced electric-driven PCMs are explored. PCMs hold great promise for multifunctional device development, including applications in non-volatile memory, optical data storage, photonics, energy harvesting, biomedical technology, neuromorphic computing, thermal management, and flexible electronics.

关键词： Physics Applied physics materials application

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Characterization of Gap Pad Thermal Interface Material by Enabling Pressure and Thermal Mapping Capabilities

Characterization of Gap Pad Thermal Interface Material by En...

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Intersociety conference on Thermal and Thermomechanical Phenomena in Electronic systems (ITHERM)

作者： Jose Diaz Marin Melissa Rodriguez Vargas Intel Corporation Belen Costa Rica

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364347

Most electronic components generating heat will require external cooling solutions to keep device temperatures within specified limits, ensuring optimal performance and reducing degradation over time. The typical solution utilizes a heatsink or spreader to cool the component in combination with a Thermal Interface Material (TIM). Thermal Interface materials are widely used in the tech industry and enable improved heat transfer to achieve reliable operation of electronic devices. In some instances, gap pads are the preferred TIM because of their multiple benefits such as being able to accommodate uneven air gaps and rough surfaces, as well as their ease of application. Typically, gap pads are used for low-power components cooled through the primary thermal solution, such as power delivery components or microprocessors with low-power *** Personal computing (PC) cooling applications, it is necessary to make accurate predictions of gap pad performance for a variety of power scenarios. This broad range of power scenarios means the hotspot can be located anywhere within the electronic component's surface. When applying a gap pad between two surfaces, it is known that the gap pad thickness is not uniform across the totality of the surfaces, and that there will be a thickness profile. This happens for multiple reasons including surface warpage of the surfaces. Because of this, assuming a uniform gap pad performance (or conductivity) can translate into significant *** this paper, the authors present how to capture local variations of gap pad performance that enable accurate modeling for multiple power scenarios, regardless of the hotspot location. This was done using a realistic, system-like test setup, capturing real pressures with pressure-mapping sensor and direct heating from silicon test chips. As the material is heated the pressure-mapping sensor was used to capture the load and pressure changes over the entire heating area. Parallelly, embedded senso

关键词： Heating systems Temperature measurement Industries Temperature sensors Accuracy Cooling Thermal resistance Surface resistance Predictive models Silicon

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MaGnn: Binary-Source Code Matching by Modality-Sharing Graph Convolution for Binary Provenance Analysis

MaGnn: Binary-Source Code Matching by Modality-Sharing Graph...

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IEEE Annual International Computer Software and applications conference (COMPSAC)

作者： Weihan Ou Steven H. H. Ding School of Computing Queen’s University Kingston Canada

The number and variety of binaries running on electrical devices, public clouds, and on-premise infrastructure have been increasing rapidly. Recent successful supply chain attacks indicate that even for binaries known to be developed by trustful developers, they can still contain malicious functionalities and copy-and-pasted vulnerabilities that pose security risks to operational systems and end users. By analyzing the origin of a target code, code provenance analysis helps to relieve such problem by revealing information about the origin of a binary sample such as the author or the included software bill-of-materials. Since in most cases source symbol information is removed during the compilation process, given a binary code sample, matching it to its corresponding source code could improve the accuracy and efficiency of the provenance analysis. Existing binary-source code matching methods focus on comparing manually selected code literals (e.g. the number of if/else statements). However, these methods suffer from the issue of generalizability and require significant manual *** from the previous methods, we propose a machine learning-based binary-source code matching system, MaGnn, which measures the consistency of an input binary-source code pair by automatically extracting high-dimensional feature representations of the input and calculating the functionality similarity. With the Siamese architecture that shares a unified encoder across two modalities, McGnn is able to calculate the similarity of the input binary-source code pair with the automatically-extracted functionality representations. With the graph convolution neural network as the representation encoder, MaGnn is able to learn and encode the functionality information of the input pairs from their graph features into high-dimensional representation vectors. We benchmark MaGnn with a state-of-the-art binary-source code matching method and two machine-learning models on six out-of-sample data

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AcouDL: Context-Aware Daily Activity Recognition from Natural Acoustic Signals

AcouDL: Context-Aware Daily Activity Recognition from Natura...

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International conference on Smart computing (SMARTCOMP)

作者： Avijoy Chakma Anirban Das Abu Zaher Md Faridee Suchetana Chakraborty Sandip Chakraborty Nirmalya Roy Dept. of Computer Science Bowie State University USA Dept. of CSE NIIT University India Amazon USA Dept.of CSE IIT Jodhpur India Dept.of CSE IIT Kharagpur India Information Systems UMBC USA

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

The ubiquitousness of smart and wearable devices with integrated acoustic sensors in modern human lives presents tremendous opportunities for recognizing human activities in our living spaces through ML-driven applications. However, their adoption is often hindered by the requirement of large amounts of labeled data during the model training phase. Integration of contextual metadata has the potential to alleviate this since the nature of these meta-data is often less dynamic (e.g. cleaning dishes, and cooking both can happen in the kitchen context) and can often be annotated in a less tedious manner (a sensor always placed in the kitchen). However, most models do not have good provisions for the integration of such meta-data information. Often, the additional metadata is leveraged in the form of multi-task learning with sub-optimal outcomes. On the other hand, reliably recognizing distinct in-home activities with similar acoustic patterns (e.g. chopping, hammering, knife sharpening) poses another set of challenges. To mitigate these challenges, we first show in our preliminary study that the room acoustics properties such as reverberation, room materials, and background noise leave a discernible fingerprint in the audio samples to recognize the room context and proposed AcouDL as a unified framework to exploit room context information to improve activity recognition performance. Our proposed self-supervision-based approach first learns the context features of the activities by leveraging a large amount of unlabeled data using a contrastive learning mechanism and then incorporates this feature induced with a novel attention mechanism into the activity classification pipeline to improve the activity recognition performance. Extensive evaluation of AcouDL on three datasets containing a wide range of activities shows that such an efficient feature fusion-mechanism enables the incorporation of metadata that helps to better recognition of the activities under challenging

关键词： Training Attention mechanisms Contrastive learning Activity recognition Metadata Fingerprint recognition Data augmentation

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Industry-Oriented Lightweight Simulation System

Industry-Oriented Lightweight Simulation System

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International conference on Parallel and Distributed systems (ICPADS)

作者： Changwei Liu Hao Ren Nan Zhou Guoqiang Li XiaoJun Liang Weihua Gui Chunhua Yang School of Future Technology South China University of Technology Guangzhou China Department of Strategic and Advanced Interdisciplinary Research Peng Cheng Laboratory Shenzhen China School of Materials Science and Engineering South China University of Technology Guangzhou China School of Automation Central South University Chang Sha China

In order to address the simulation requirements arising from limited computational resources, non-reusable complex models, and low-latency demands for edge devices within the realm of the Industrial Internet of Things (IIoT), we have successfully defined and developed a Lightweight Simulation System(LSS) specifically designed for industrial applications. This system draws inspiration from the architectural principles of edge and fog computing. LSS leverages an innovative architectural paradigm known as Function as a Service (FaaS), which demonstrates an even finer granularity compared to the traditional microservices architecture. Within the framework of LSS, we have discretely compartmentalized distinct operational elements, such as data acquisition and storage, model computation, and model visualization, into Docker microservices. This strategic shift in design effectively alleviates the need for developers to be overly concerned with the intricacies of server management and domain-specific industrial knowledge. Consequently, this enables them to shift their focus towards determining "what needs to be accomplished," thereby transforming the granularity of work from being server-centric to a more task-centric perspective. In comparison to simulation platforms within other frameworks, LSS distinguishes itself through its remarkable attributes, which include a lightweight structure, minimal coupling, and superior real-time performance. These unique qualities substantially enhance the collaborative, shared, and reusable capabilities of simulation systems across various dimensions. To substantiate the viability of LSS, this paper provides empirical evidence by applying it to Electrolysis and partial cascade algorithms in the context of hydrometallurgy. The results underscore the fact that the LSS system significantly improves modeling efficiency, fosters the reusability of models and algorithms, and elevates the level of industrial automation across diverse processes.

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Germanium-based nearly hyperuniform nanoarchitectures by ion beam impact

arXiv

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arXiv 2023年

作者： Claude, Jean-Benoit Bouabdellaoui, Mohammed Wenger, Jerome Bollani, Monica Salvalaglio, Marco Abbarchi, Marco Aix Marseille Univ CNRS Centrale Marseille Institut Fresnel Marseille13013 France Aix Marseille Univ Université de Toulon CNRS IM2NP Marseille France Istituto di Fotonica e Nanotecnologie-Consiglio Nazionale delle Ricerche Laboratory for Nanostructure Epitaxy and Spintronics on Silicon Via Anzani 42 Como22100 Italy Institute of Scientific Computing TU Dresden Dresden01062 Germany TU Dresden Dresden01062 Germany Solnil 95 Rue de la République Marseille13002 France

We address the fabrication of nano-architectures by impacting thin layers of amorphous Ge deposited on SiO2 with a Ga+ ion beam and investigate the structural and optical properties of the resulting patterns. By adjusting beam current and scanning parameters, different classes of nano-architectures can be formed, from elongated and periodic structures to disordered ones with a footprint of a few tens of nm. The latter disordered case features a significant suppression of large length scale fluctuations that are conventionally observed in ordered systems and exhibits a nearly hyperuniform character, as shown by the analysis of the spectral density at small wave vectors. It deviates from conventional random fields as accounted for by the analysis of Minkowski functionals. A proof of concept for potential applications is given by showing peculiar reflection properties of the resulting nano-structured films that exhibit colorization and enhanced light absorption with respect to the flat Ge layer counterpart (up to one order of magnitude at some wavelength). This fabrication method for disordered hyperuniform structures does not depend on the beam size. Being ion beam technology widely adopted in semiconductor foundries over 200 mm wafers, our work provides a viable pathway for obtaining disordered, nearly-hyperuniform materials by self-assembly with a footprint of tens of nanometers for electronic and photonic devices, energy storage and sensing. Copyright © 2023, The Authors. All rights reserved.

关键词： Ion beams

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Time-Delayed Reservoir computing Based on Dual-Waveband Quantum-Dot Spin-Polarized Vertical Cavity Surface-Emitting Laser

arXiv

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arXiv 2022年

作者： Skontranis, M. Sarantoglou, G. Bogris, A. Mesaritakis, Charis University of the Aegean Dept. of Information and Communication Systems Engineering Palama 2 Karlovassi Samos 83200 Greece University of West Attica Dept. of Informatics and Computer Engineering Aghiou Spiridonos Egaleo Athens12243 Greece

In this work we present numerical results concerning a time-delayed reservoir computing scheme, where its single nonlinear node, is a Quantum-Dot spin polarized Vertical Cavity Surface-Emitting Laser (QD s-VCSEL). The proposed photonic neuromorphic scheme, exploits the complex temporal dynamics of multiple energy states present in quantum dot materials and uses emission from two discrete wavebands and two polarization states, so as to enhance computational efficiency. The benchmark task used for this architecture, is the equalization of a distorted 25Gbaud PAM-4 signal after 50Km of transmission at 1550nm. Results confirm that although typical ground-state emitting quantum-dot nodes offer limited performance, due to bandwidth limitations;by exploiting dual emission, we achieved a one-hundred-fold improvement in bit-error rate. This performance boost can pave the way for the infiltration of quantum-dot based devices in high-speed demanding neuromorphic driven applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Semiconductor quantum dots

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