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| 1 | Orbital angular momentum mode logical operation using optical diffractive neural network显示文摘Optical logical operations demonstrate the key role of optical digital computing,which can perform general-purpose calculations and possess fast processing speed,low crosstalk,and high throughput.The logic states usually refer to linear momentums that are distinguished by intensity distributions,which blur the discrimination boundary and limit its sustainable applications.Here,we introduce orbital angular momentum(OAM)mode logical operations performed by optical diffractive neural networks(ODNNs).Using the OAM mode as a logic state not only can improve the parallel processing ability but also enhance the logic distinction and robustness of logical gates owing to the mode infinity and orthogonality.ODNN combining scalar diffraction theory and deep learning technology is designed to independently manipulate the mode and spatial position of multiple OAM modes,which allows for complex multilight modulation functions to respond to logic inputs.We show that few-layer ODNNs successfully implement the logical operations of AND,OR,NOT,NAND,and NOR in simulations.The logic units of XNOR and XOR are obtained by cascading the basic logical gates of AND,OR,and NOT,which can further constitute logical half-adder gates.Our demonstrations may provide a new avenue for optical logical operations and are expected to promote the practical application of optical digital computing. | PEIPEI WANG WENJIE XIONG ZEBIN HUANG YANLIANG HE ZHIQIANG XIE JUNMIN LIU HUAPENG YE YING LI DIANYUAN FAN SHUQING CHEN | 2021 | Photonics Research2021，9，10： | 5 |
| 2 | Power Density Distribution Simulation and Relevant Heat Effect Calculation of a High Power CO2 laser with Low Order modes显示文摘 | Li Junchang Ma Kun Fan Zebin | 2002 | Chinese Journal of Lasers2002，11，5： | 1 |
| 3 | Application of phase unwrapping algorithm based on least-squares and it- eration in digital holography 显示文摘 | Xia Haiting Guo Rongxin Fan Zebin | 2010 | SPIE 78482010，，： | 1 |
| 4 | Power density distribution simulation and relevant heat effect calculation of a high - power CO2 laser with low order modes显示文摘 | Li Junchang Ma Kun Fan Zebin | 2002 | Chinese Journal of Lasers B ( English Edition) October2002，11，5： | 1 |
| 5 | SEM imaging of insulating specimen through a transparent conducting veil of carbon nanotube显示文摘Observing the morphology of insulating specimen in scanning electron microscope(SEM)is of great significance for the nanoscale semiconductor devices and biological tissues.However,the charging effect will cause image distortion and abnormal contrast when observing insulating specimen in SEM.A typical solution to this problem is using metal coating or water-removable conductive coating.Unfortunately,in both cases the surface of the specimen is covered by a thin layer of conductive material which hides the real surface morphology and is very difficult to be completely removed after imaging.Here we show a convenient,residue-free,and versatile method to observe real surface morphology of insulating specimen without charging effect in SEM with the help of a nanometer-thick film of super-aligned carbon nanotube(SACNT).This thin layer of SACNT film,like metal,can conduct the surface charge on insulating specimen through the sample stage to the ground,thus eliminating the charging effect.SACNT film can also be used as the conductive tape to carry and immobilize insulating powder or particles during SEM imaging.Different from the metal coating,SACNT film is transparent,so that the real microstructure of the insulating specimen surface can be observed.In addition,SACNT film can be easily attached to and peeled off from the surface of specimen without any residue.This convenient,residue-free,and versatile method can open up new possibilities in nondestructive SEM imaging of a wide variety of insulating materials,semiconductor devices,and biological tissues. | Xinyu Gao Guo Chen He Ma Yuchen Ju Ke Zhang Lin Cong Wen Ning Zi Yuan Zebin Liu Lina Zhang Peng Liu Shoushan Fan Kaili Jiang | 2022 | Nano Research2022，15，7： | 0 |
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