Holography, which was invented by Dennis Gabor in 1948, offers an approach to reconstructing both the amplitude and phase information of a three-dimensional (3D) object [1]. Since its invention, the concept of hologra...Holography, which was invented by Dennis Gabor in 1948, offers an approach to reconstructing both the amplitude and phase information of a three-dimensional (3D) object [1]. Since its invention, the concept of holography has been widely used in various fields, such as microscopy [2], interferometry [3], ultrasonography [4], and holographic display [5]. Optical holography can be divided into two steps: recording and reconstruction. A conventional hologram is recorded onto a photosensitive film as the interference between an object beam carrying the 3D object information and a reference beam. Thereafter, the original object wavefront is reconstructed in the 3D image space by illuminating the reference beam on the recorded hologram.展开更多
Optical machine learning has emerged as an important research area that,by leveraging the advantages inherent to optical signals,such as parallelism and high speed,paves the way for a future where optical hardware can...Optical machine learning has emerged as an important research area that,by leveraging the advantages inherent to optical signals,such as parallelism and high speed,paves the way for a future where optical hardware can process data at the speed of light.In this work,we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks.We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption.The decryptors,designed for operation in the near-infrared region,are nanoprinted on complementary metal-oxide-semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm achieving a neuron density of>500 million neurons per square centimetre.This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption3,sensing4,medical diagnostics5 and computing6,7.展开更多
Neuromorphic computing applies concepts extracted from neuroscience to developdevices shaped like neural systems and achieve brain-like capacity and efficiency. Inthis way, neuromorphic machines, able to learn from th...Neuromorphic computing applies concepts extracted from neuroscience to developdevices shaped like neural systems and achieve brain-like capacity and efficiency. Inthis way, neuromorphic machines, able to learn from the surrounding environmentto deduce abstract concepts and to make decisions, promise to start a technologicalrevolution transforming our society and our life. Current electronic implementationsof neuromorphic architectures are still far from competing with their biologicalcounterparts in terms of real-time information-processing capabilities, packingdensity and energy efficiency. A solution to this impasse is represented by theapplication of photonic principles to the neuromorphic domain creating in this waythe field of neuromorphic photonics. This new field combines the advantages ofphotonics and neuromorphic architectures to build systems with high efficiency,high interconnectivity and high information density, and paves the way to ultrafast,power efficient and low cost and complex signal processing. In this Perspective, wereview the rapid development of the neuromorphic computing field both in theelectronic and in the photonic domain focusing on the role and the applications ofmemristors. We discuss the need and the possibility to conceive a photonicmemristor and we offer a positive outlook on the challenges and opportunities forthe ambitious goal of realising the next generation of full-optical neuromorphichardware.展开更多
基金support from the Australian Research Council (ARC) through the Discovery Project (DP180102402)support from a scholarship from theChina Scholarship Council (201706190189)financial support from the Humboldt Research Fellowship from the Alexander von Humboldt Foundation
文摘Holography, which was invented by Dennis Gabor in 1948, offers an approach to reconstructing both the amplitude and phase information of a three-dimensional (3D) object [1]. Since its invention, the concept of holography has been widely used in various fields, such as microscopy [2], interferometry [3], ultrasonography [4], and holographic display [5]. Optical holography can be divided into two steps: recording and reconstruction. A conventional hologram is recorded onto a photosensitive film as the interference between an object beam carrying the 3D object information and a reference beam. Thereafter, the original object wavefront is reconstructed in the 3D image space by illuminating the reference beam on the recorded hologram.
基金The authors thank Shiwei Zhang and Prof.Xiaodong Li for their enlightening discussions.The authors acknowledge the use of facilities within the RMIT Microscopy and Microanalysis Facility(RMMF)the support of the Shanghai Institute of Intelligent Science and Technology,Tongji University,Shanghai,China.M.G.acknowledges the funding support from the Zhangjiang National Innovation Demonstration Zone(ZJ2019-ZD-005).
文摘Optical machine learning has emerged as an important research area that,by leveraging the advantages inherent to optical signals,such as parallelism and high speed,paves the way for a future where optical hardware can process data at the speed of light.In this work,we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks.We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption.The decryptors,designed for operation in the near-infrared region,are nanoprinted on complementary metal-oxide-semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm achieving a neuron density of>500 million neurons per square centimetre.This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption3,sensing4,medical diagnostics5 and computing6,7.
文摘Neuromorphic computing applies concepts extracted from neuroscience to developdevices shaped like neural systems and achieve brain-like capacity and efficiency. Inthis way, neuromorphic machines, able to learn from the surrounding environmentto deduce abstract concepts and to make decisions, promise to start a technologicalrevolution transforming our society and our life. Current electronic implementationsof neuromorphic architectures are still far from competing with their biologicalcounterparts in terms of real-time information-processing capabilities, packingdensity and energy efficiency. A solution to this impasse is represented by theapplication of photonic principles to the neuromorphic domain creating in this waythe field of neuromorphic photonics. This new field combines the advantages ofphotonics and neuromorphic architectures to build systems with high efficiency,high interconnectivity and high information density, and paves the way to ultrafast,power efficient and low cost and complex signal processing. In this Perspective, wereview the rapid development of the neuromorphic computing field both in theelectronic and in the photonic domain focusing on the role and the applications ofmemristors. We discuss the need and the possibility to conceive a photonicmemristor and we offer a positive outlook on the challenges and opportunities forthe ambitious goal of realising the next generation of full-optical neuromorphichardware.
