  Optical computing is considered a promising solution for the growing demand
for parallel computing in various cutting-edge fields, requiring high
integration and high speed computational capacity. In this paper, we propose a
novel optical computation architecture called diffraction casting (DC) for
flexible and scalable parallel logic operations. In DC, a diffractive neural
network (DNN) 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 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 bits 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, assuring high
scalability and integration capability. Our study offers a novel design
architecture for optical computers and paves the way for a next-generation
optical computing paradigm.
