检索结果-内蒙古大学图书馆

photonic computing: from materials and devices to systems and applications 2024

ISBN: (纸本)9781510678866

The proceedings contain 10 papers. The topics discussed include: cryogenic integrated optical modulators for classical and quantum computing;orbital angular momentum beams multiplexing and demultiplexing using hybrid optical-electronic neural network;optical data compression and hashing via 4f-based reconfigurable complex convolution module;energy-efficient optical memory architecture using phase change memory: clock frequency trade-offs for edge computing;Intersubband engineered nonlinear activation function for photonic neural networks;interpretable inverse-designed cavity for nonlinear photon pair generation on-chip;probing ring resonator sensor based on vernier effect;enhanced photonic accelerator design for efficient vector-matrix multiplication in deep learning;and weight bank addition photonic accelerator for artificial intelligence.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fabrication and integration of photonic devices for phase-change memory and neuromorphic computing

引用

International Journal of Extreme Manufacturing 2024年第2期6卷 2-27页

作者： Wen Zhou Xueyang Shen Xiaolong Yang Jiangjing Wang Wei Zhang Center for Alloy Innovation and Design(CAID) State Key Laboratory for Mechanical Behavior of MaterialsXi’an Jiaotong UniversityXi’an 710049People’s Republic of China

In the past decade,there has been tremendous progress in integrating chalcogenide phase-change materials(PCMs)on the silicon photonic platform for non-volatile memory to neuromorphic in-memory computing *** particular,these non von Neumann computational elements and systems benefit from mass manufacturing of silicon photonic integrated circuits(PICs)on 8-inch wafers using a 130 nm complementary metal-oxide semiconductor *** manufacturing based on deep-ultraviolet lithography and electron-beam lithography enables rapid prototyping of PICs,which can be integrated with high-quality PCMs based on the wafer-scale sputtering technique as a back-end-of-line *** this article,we present an overview of recent advances in waveguide integrated PCM memory cells,functional devices,and neuromorphic systems,with an emphasis on fabrication and integration processes to attain state-of-the-art device *** a short overview of PCM based photonic devices,we discuss the materials properties of the functional layer as well as the progress on the light guiding layer,namely,the silicon and germanium waveguide ***,we discuss the cleanroom fabrication flow of waveguide devices integrated with thin films and nanowires,silicon waveguides and plasmonic microheaters for the electrothermal switching of PCMs and mixed-mode ***,the fabrication of photonic and photonic–electronic neuromorphic computing systems is *** systems consist of arrays of PCM memory elements for associative learning,matrix-vector multiplication,and pattern *** large-scale integration,the neuromorphic photonic computing paradigm holds the promise to outperform digital electronic accelerators by taking the advantages of ultra-high bandwidth,high speed,and energy-efficient operation in running machine learning algorithms.

关键词： nanofabrication silicon photonics phase-change materials non-volatile photonic memory neuromorphic photonic computing

来源：评论

学校读者我要写书评

暂无评论

Nonvolatile photonics based on phase change materials: where are the limits?

Nonvolatile photonics based on phase change materials: where...

引用

Conference on photonic computing - from materials and devices to systems and applications

作者： Li, Junying Hu, Juejun Univ Chinese Acad Sci Hangzhou Inst Adv Study Hangzhou Peoples R China MIT Dept Mat Sci & Engn Cambridge MA 02139 USA

ISBN: (纸本)9781510678866;9781510678873

Integration of chalcogenide phase-change material (PCM) with photonic circuits offers a practical route of introducing nonvolatile optical memories-a key enabling capability for optical neuromorphic computing. The prospect has motivated a surge of research efforts in this field and significant improvements in the performance of PCM-based photonic devices. These advances prompt an important question: what are the ultimate performances that can be achieved in PCM-based photonic devices? Here we address the question by quantitatively analyzing performance bounds of PCM memories on optical loss, crosstalk, energy consumption, and multi-level operation.

关键词： Phase change materials integrated photonics reconfigurable photonics photonic memories

来源：评论

学校读者我要写书评

暂无评论

Adjoint-Optimized Logic Gates for photonic computing

引用

JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第22期42卷 8032-8041页

作者： Gangaraj, S. Ali Hassani Nguyen, Dan T. Corning Res & Dev Opt Phys Div Sullivan Pk Corning NY 14831 USA

Logic gates are crucial in computing as they facilitate information processing. Various gates, from basic AND, OR and X gates to more complex C-NOT gates are essential for the evolution of the computing systems either in classical realm or quantum technology. Here, we introduce photonic logic gates using adjoint-method-assisted inverse design optimization. These gates, accommodating both single and multi-photonic bit configurations, enable fundamental logic operations such as the X-gate, AND-gate, OR-gate, and more complex operations like the C-NOT gate. Notably, the proposed C-NOT gates are individually designed as standalone optical devices, avoiding modular assembly. These photonic gates are high-fidelity, compact (typically within $3\lambda \times 4\lambda$), suitable for integration into photonic circuits for optical computing. Importantly, they require no external bias for activation or control, ensuring low power consumption. The proposed gates are composed of linear optical materials like silicon/silicon dioxide, allowing routine photonic technology fabrication methods for realization.

关键词： Logic gates photonics Optical waveguides Optimization Optical device fabrication Splines (mathematics) Optical materials Adjoint optimization C-NOT gate inverse design logic gate photonic computing

来源：评论

学校读者我要写书评

暂无评论

A Survey on Optical Phase-Change Memory: The Promise and Challenges

引用

IEEE ACCESS 2023年 11卷 11781-11803页

作者： Shafiee, Amin Pasricha, Sudeep Nikdast, Mahdi Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA

Silicon photonics (SiPh) technology has facilitated the deployment of integrated photonics across different application domains, from ultra-fast communication in Datacom applications to energy-efficient optical computation in emerging hardware accelerators for machine learning. More recently, the integration of SiPh and phase change materials has created a unique opportunity to realize adaptable, reconfigurable, and programmable photonic platforms. In particular, the nonvolatile programmability in phase change materials has made them a promising candidate for implementing photonic memory cells and architectures. Accordingly, photonic memory systems and even in-memory photonic computing paradigms are on the rise, especially given their potential for improving data access in electronic and photonic processors. However, there are still many challenges in the design and fabrication of phase-change photonic integrated circuits, which need to be addressed. This article presents a comprehensive survey on the recent advances and challenges for the integration of phase change materials with contemporary photonic devices while focusing on the photonic memory application. In particular, we explore phase-change photonic memory from the material level to the architecture level by presenting an overview of different material-level characteristics of phase change materials with their optical, electrical, and thermal properties as well as their integration into SiPh devices and photonic memory architectures and their application for in-memory photonic computing. We also present a comparison with electronic memory and discuss open research challenges that must be addressed to further advance phase-change photonic memory towards successful integration into emerging computing systems.

关键词： photonics Phase change materials Phase change memory Nonvolatile memory Optical sensors Optical pulses Adaptive optics phase-change memory in-memory photonic computing photonic integrated circuits silicon photonics

来源：评论

学校读者我要写书评

暂无评论

Weight Bank Addition photonic Accelerator for Artificial Intelligence

Weight Bank Addition Photonic Accelerator for Artificial Int...

引用

Conference on photonic computing - from materials and devices to systems and applications

作者： Zhang, Wenwen Zhang, Hao Wang, Jing Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA USA Univ British Columbia Dept Mech Engn Vancouver BC Canada

ISBN: (纸本)9781510678866;9781510678873

Neural networks, a potent data-driven method for artificial intelligence, play a crucial role in modern estimation and classification tasks. However, they often encounter computational constraints. To overcome these, we propose photonics-based neuromorphic networks, offering faster processing than electronic systems. We focus on the weight bank, crucial for matrix multiplication, utilizing parallel cascaded micro-ring resonators (MRRs). Our study on silicon on insulators (SOI) demonstrates how cascaded MRRs address cross-talk issues in wavelength division multiplexing (WDM) systems. Additionally, we design a silicon photonic accelerator for weight addition, optimized for speed and energy efficiency, providing comparable performance to electronic devices.

关键词： Artificial intelligence micro-ring resonators weight bank

来源：评论

学校读者我要写书评

暂无评论

Intersubband engineered nonlinear activation function for photonic neural networks

Intersubband engineered nonlinear activation function for ph...

引用

Conference on photonic computing - from materials and devices to systems and applications

作者： Xu, Zheheng Addamane, Sadhvikas J. Burghoff, David Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Sandia Natl Labs Albuquerque NM 87123 USA

ISBN: (纸本)9781510678866;9781510678873

Fast, sensitive, and compact photonic nonlinear activation devices are crucial for forming photonic neural networks with high computational efficiency. However, conventional photonic activation units lack suitable nonlinearity due to weak optical nonlinearities. Using intersubband nanostructures, we introduce the smallest possible optoelectronic approach for nonlinear processing in photonic neural networks. Here we also demonstrate the simulation and fabrication of the intersubband nanostructures. The simulation results shows a 3.5 microwatt threshold and an optical response time of 10 picoseconds within a device that is only 4 microns in length.

关键词： Intersubband activation neural networks nonlinear

来源：评论

学校读者我要写书评

暂无评论

2D multifunctional devices:from material preparation to device fabrication and neuromorphic applications

引用

International Journal of Extreme Manufacturing 2024年第3期6卷 91-118页

作者： Zhuohui Huang Yanran Li Yi Zhang Jiewei Chen Jun He Jie Jiang Hunan Key Laboratory of Nanophotonics and Devices School of PhysicsCentral South UniversityChangshaHunanPeople’s Republic of China Department of Applied Physics The Hong Kong Polytechnic UniversityHong Kong Special Administrative Region of ChinaPeople’s Republic of China

Neuromorphic computing systems,which mimic the operation of neurons and synapses in the human brain,are seen as an appealing next-generation computing method due to their strong and efficient computing ***-dimensional (2D) materials with dangling bond-free surfaces and atomic-level thicknesses have emerged as promising candidates for neuromorphic computing *** a result,2D neuromorphic devices may provide an ideal platform for developing multifunctional neuromorphic ***,we review the recent neuromorphic devices based on 2D material and their multifunctional *** synthesis and next micro–nano fabrication methods of 2D materials and their heterostructures are first *** recent advances of neuromorphic 2D devices are discussed in detail using different operating *** importantly,we present a review of emerging multifunctional neuromorphic applications,including neuromorphic visual,auditory,tactile,and nociceptive systems based on 2D *** the end,we discuss the problems and methods for 2D neuromorphic device developments in the *** paper will give insights into designing 2D neuromorphic devices and applying them to the future neuromorphic systems.

关键词： 2D material micro–nano fabrication multifunctional system neuromorphic electronics artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Energy-efficient optical memory architecture using phase change memory: Clock frequency trade-offs for edge computing

Energy-efficient optical memory architecture using phase cha...

引用

Conference on photonic computing - from materials and devices to systems and applications

作者： Jahannia, Belal Ye, Jiachi Altaleb, Salem Cai, Qian Madadi-Kandjani, Ehsan Asadizanjani, Navid Patil, Chandraman Heidari, Elham Dalir, Hamed Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

ISBN: (纸本)9781510678866;9781510678873

Developing energy-efficient optical components for computing is crucial for AI-driven hardware technologies. While previous studies primarily focused on optimizing cache-level design and managing write-intensive memory addresses, the impact of clock frequency on the energy consumption of emerging memory technologies, such as PCM, remains underexplored. In this work, through comprehensive simulation-based analysis, we reveal the complex relationship between clock frequency and the energy efficiency of OPCM, SRAM, and DRAM. The proposed memory architecture has demonstrated the potential to reduce overall energy consumption by up to 75% for the MiBench benchmark suite, a widely used set of embedded systems and IoT workloads. This work contributes to the ongoing efforts to improve the energy efficiency of optical computing systems, a critical factor in realizing the full potential of these emerging technologies.

关键词： PCM Energy-efficient computing Edge computing memory architectures

来源：评论

学校读者我要写书评

暂无评论

Enhanced photonic Accelerator Design for Efficient Vector-Matrix Multiplication in Deep Learning

Enhanced Photonic Accelerator Design for Efficient Vector-Ma...

引用

Conference on photonic computing - from materials and devices to systems and applications

作者： Wang, Jing Zhang, Hao Zhang, Wenwen Univ British Columbia Dept Mech Engn Vancouver BC Canada Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA USA

ISBN: (纸本)9781510678866;9781510678873

Matrix multiplication with weights is a fundamental operation in deep neural networks, crucial for tasks reliant on data-driven modeling. However, computational limitations often hinder these operations. To address this, we propose photonics neural networks, offering faster processing speeds compared to electronic systems. Our paper aims to develop two distinct photonic neural networks. We use micror-ing resonators as neurons, exploiting their varied transmission properties for weight updates. Our experiments achieved a maximum transmission drop of approximately 0.7 dBm and a maximum spectral shift of around 1 nm per voltage change. Furthermore, we propose and explore two methods for implementing summation and non-linear activation using photonics devices with various materials, investigating the design constraints associated with each approach. Our experimental results corroborate our simulation predictions, validating the feasibility of our designs.

关键词： Microrings Neurons Design Artificial neural networks Photodetectors Modulators Waveguides Wavelength division multiplexing Microresonators Resonators

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：