第8届国际先进资讯科技学术会议（The 8th International Conference on Advanced Infocomm Technology）于2015年10月25～27日在杭州举行。此次会议由浙江大学和中国卫星海上测控部联合主办，浙江大学现代光学仪器国家重点实验室、浙...第8届国际先进资讯科技学术会议（The 8th International Conference on Advanced Infocomm Technology）于2015年10月25～27日在杭州举行。此次会议由浙江大学和中国卫星海上测控部联合主办，浙江大学现代光学仪器国家重点实验室、浙江大学光子学与技术国际合作联合实验室共同承办。展开更多
An averaged field approach is suggested for obtaining the band structure of a photonic crystal when the dimension of the inclusions is much smaller compared with the longest period of the rectangular lattice of the ph...An averaged field approach is suggested for obtaining the band structure of a photonic crystal when the dimension of the inclusions is much smaller compared with the longest period of the rectangular lattice of the photonic crystal.The method is illustrated for the H-polarization of the 2-dimensional case.The band structure is obtained in an explicit and simple way.The method is verified numerically by comparing with the conventional plane-wave expansion method.展开更多
Artificial neural networks have dramatically improved the performance of many machine-learning applications such as image recognition and natural language processing. However, the electronic hardware implementations o...Artificial neural networks have dramatically improved the performance of many machine-learning applications such as image recognition and natural language processing. However, the electronic hardware implementations of the above-mentioned tasks are facing performance ceiling because Moore’s Law is slowing down. In this article, we propose an optical neural network architecture based on optical scattering units to implement deep learning tasks with fast speed, low power consumption and small footprint.The optical scattering units allow light to scatter back and forward within a small region and can be optimized through an inverse design method. The optical scattering units can implement high-precision stochastic matrix multiplication with mean squared error < 10-4 and a mere 4*4 um2 footprint.Furthermore, an optical neural network framework based on optical scattering units is constructed by introducing "Kernel Matrix", which can achieve 97.1% accuracy on the classic image classification dataset MNIST.展开更多
文摘第8届国际先进资讯科技学术会议（The 8th International Conference on Advanced Infocomm Technology）于2015年10月25～27日在杭州举行。此次会议由浙江大学和中国卫星海上测控部联合主办，浙江大学现代光学仪器国家重点实验室、浙江大学光子学与技术国际合作联合实验室共同承办。
基金Supported in part by the grant for the Chang Jiang special Professor(Ministry of Education,P.R.China)。
文摘An averaged field approach is suggested for obtaining the band structure of a photonic crystal when the dimension of the inclusions is much smaller compared with the longest period of the rectangular lattice of the photonic crystal.The method is illustrated for the H-polarization of the 2-dimensional case.The band structure is obtained in an explicit and simple way.The method is verified numerically by comparing with the conventional plane-wave expansion method.
基金This work was supported by the National Key Research and Development Program of China(2017YFA0205700)the National Natural Science Foundation of China(61927820)Yurui Qu was supported by Zhejiang Lab’s International Talent Fund for Young Professionals.
文摘Artificial neural networks have dramatically improved the performance of many machine-learning applications such as image recognition and natural language processing. However, the electronic hardware implementations of the above-mentioned tasks are facing performance ceiling because Moore’s Law is slowing down. In this article, we propose an optical neural network architecture based on optical scattering units to implement deep learning tasks with fast speed, low power consumption and small footprint.The optical scattering units allow light to scatter back and forward within a small region and can be optimized through an inverse design method. The optical scattering units can implement high-precision stochastic matrix multiplication with mean squared error < 10-4 and a mere 4*4 um2 footprint.Furthermore, an optical neural network framework based on optical scattering units is constructed by introducing "Kernel Matrix", which can achieve 97.1% accuracy on the classic image classification dataset MNIST.
