Optical Design of a Smart-Pixel-Based Optical Convolutional Neural Network

초록

We designed lens systems for a smart-pixel-based optical convolutional neural network (SPOCNN) using optical software to analyze image spread and estimate alignment tolerance for various kernel sizes. The design, based on a three-element lens, was reoptimized to minimize spot size while meeting system constraints. Simulations included root mean square spot and encircled energy diagrams, showing that geometric aberration increases with the scale factor, while diffraction effect remains constant. Alignment tolerance was determined by combining geometric image size with image spread analysis. While the preliminary scaling analysis predicted a limit at a kernel array size of 66 x 66, simulations showed that a size of 61 x 61 maintains sufficient alignment tolerance, well above the critical threshold. The discrepancy is likely due to lower angular aberration in the simulated optical design. This study confirms that an array size of 61 x 61 is feasible for SPOCNN, validating the scaling analysis for predicting image spread trends caused by aberration and diffraction.

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