Diffraction casting

Optical computing is considered a promising solution for the growing demand for parallel computing in various cutting-edge fields that require high integration and high-speed computational capacity. We propose an optical computation architecture called diffraction casting (DC) for flexible and scalable parallel logic operations. In DC, a diffractive neural network is designed for single instruction, multiple data (SIMD) operations. This approach allows for the alteration of logic operations simply by changing the illumination patterns. Furthermore, it eliminates the need for encoding and decoding of the input and output, respectively, by introducing a buffer around the input area, facilitating end-to-end all-optical computing. We numerically demonstrate DC by performing all 16 logic operations on two arbitrary 256-bit parallel binary inputs. Additionally, we showcase several distinctive attributes inherent in DC, such as the benefit of cohesively designing the diffractive elements for SIMD logic operations that assure high scalability and high integration capability. Our study offers a design architecture for optical computers and paves the way for a next-generation optical computing paradigm.

Optical computing is a longstanding and captivating topic in the fields of optics and photonics. It is considered a potential post-Moore computing technology1 that offers distinct advantages, including high bandwidth, rapid processing speed, low power consumption, and parallelism.2,3 Around the 1980s, optical computing was actively explored, with developments in technologies, such as optical vector matrix multipliers4– 7 and optical associative memories.8– 10 Among these, shadow casting (SC) emerged as a prominent optical computing technology of that era.11– 14 SC facilitated single instruction, multiple data (SIMD) for logical operations through optical and spatially parallel computing. The SC scheme relied on shadowgrams, which optically generated a single output image through massively parallel logic operations from two binary input images. The versatility of SIMD logic operations was attained by altering the illumination pattern of the shadowgrams. Another key aspect involved the computational encoding and decoding of input and output images, respectively, designed to balance light intensities between the zeros and ones in the binary images. This computational process was an obstacle in achieving end-to-end optical computing. Despite the anticipated benefits in speed and energy efficiency, these optical computing technologies in the 1980s stagnated due to limitations in hardware (fabrication) and software (design) for optical components at that time. As a result, they lagged behind the major progress made in electronic computing.

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