Programmable Diffractive Photonic Computing Using Pixel-Level Post-Processing for Adaptive Data Classification

This invention provides a programmable diffractive photonic computing system that uses a single diffractive surface in combination with a Complementary Metal-Oxide-Semiconductor (CMOS) pixel array for data processing and classification. Unlike traditional systems requiring spatial light modulators or multiple diffractive planes, programmability is achieved through pixel-level post-processing. By selecting and operating on specific pixels in the detector array, the system adapts to varying tasks without modifying the diffractive surface. This innovation reduces power consumption and latency while maintaining versatility for real-time applications.

Background: 
The system uses a single diffractive surface to create spatial correlations in input data, transforming it into a format suitable for classification. Pixel-level post-processing within a CMOS camera array removes the need for tunable diffractive surfaces or spatial light modulators. Machine learning algorithms optimize the selection of target pixels and their operations, allowing the system to efficiently perform diverse tasks with minimal computational load. This approach integrates diffractive photonic computing with an adaptable processing framework, making it suitable for practical and real-world scenarios.

Applications: 

• Spectral and Image Data Processing
• Context-Aware Imaging
• Energy-Efficient Photonic Computing

Advantages: 

• Achieves programmability through pixel-level post-processing
• Reduces power consumption and latency compared to traditional systems
• Avoids the need for spatial light modulators or multiple diffractive planes
• Offers flexibility and adaptability for real-world applications