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光学计算
Optical Computing
## Optical Computing ### Introduction Moore's electronic calculations are continuing to slow, and the size of electronic transistors is approaching the physical limit. Optical computing offers low power consumption, light processing speed and high throughput capability, making it an important support for high performance computing. For example, metamaterials can perform computational tasks on signals or images in an analog fashion, but use materials that only require wavelength thickness. Meta-structures have the potential to bring new changes to the field of optical analog computing in space: from free space and large systems to conceptually wavelength-sized elements, which provides a way to develop chip-based analog optical computers and computing components. The all-optical deep learning framework can perform the specific tasks of its training at the speed of light, using only optical diffraction and passive optical components or layers that do not require power, creating an efficient, fast machine implementation learning task. ### Highlights  #### Optical computing with metamaterials - Metamaterial analog computing - Performing mathematical operations (such as spatial differentiation, integration, or convolution) on the profile of an impinging wave - Possibility of miniaturized, potentially integrable, wave-based computing systems #### On-chip optical logic circuits - Optical logic operation - Inverse design of optical logic gate - On-chip integaration of optical logic elements  #### All-optical mechine learning framework - Diffractive deep neural network - Incorporating the optical nonlinearity - Application for the light-speed classification and salient object detection
