Metal halide perovskite materials have rapidly advanced in the perovskite solar cells and light-emitting diodes due to their superior optoelectronic properties. The structure of perovskite optoelectronic devices includes the perovskite active layer, electron transport layer, and hole transport layer. This indicates that the optimization process unfolds as a complex interplay between intricate chemical crystallization processes and sophisticated physical mechanisms. Traditional research in perovskite optoelectronics has mainly depended on trial-and-error experimentation, a less efficient approach. Recently, the emergence of machine learning (ML) has drastically streamlined the optimization process. Due to its powerful data processing capabilities, ML has significant advantages in uncovering potential patterns and making predictions. More importantly, ML can reveal underlying patterns in data and elucidate complex device mechanisms, playing a pivotal role in enhancing device performance. We present the latest advancements in applying ML to perovskite optoelectronic devices, covering perovskite active layers, transport layers, interface engineering, and mechanisms. In addition, it offers a prospective outlook on future developments. We believe that the deep integration of ML will significantly expedite the comprehensive enhancement of perovskite optoelectronic device performance.

Planar cameras with high performance and wide field of view (FOV) are critical in various fields, requiring highly compact and integrated technology. Existing wide FOV metalenses show great potential for ultrathin optical components, but there is a set of tricky challenges, such as chromatic aberrations correction, central bright speckle removal, and image quality improvement of wide FOV. We design a neural meta-camera by introducing a knowledge-fused data-driven paradigm equipped with transformer-based network. Such a paradigm enables the network to sequentially assimilate the physical prior and experimental data of the metalens, and thus can effectively mitigate the aforementioned challenges. An ultra-wide FOV meta-camera, integrating an off-axis monochromatic aberration-corrected metalens with a neural CMOS image sensor without any relay lenses, is employed to demonstrate the availability. High-quality reconstructed results of color images and real scene images at different distances validate that the proposed meta-camera can achieve an ultra-wide FOV (>100 deg) and full-color images with the correction of chromatic aberration, distortion, and central bright speckle, and the contrast increase up to 13.5 times. Notably, coupled with its compact size (< 0.13 cm3), portability, and full-color imaging capacity, the neural meta-camera emerges as a compelling alternative for applications, such as micro-navigation, micro-endoscopes, and various on-chip devices.

The global chip industry is grappling with dual challenges: a profound shortage of new chips and a surge of counterfeit chips valued at $75 billion, introducing substantial risks of malfunction and unwanted surveillance. To counteract this, we propose an optical anti-counterfeiting detection method for semiconductor devices that is robust under adversarial tampering features, such as malicious package abrasions, compromised thermal treatment, and adversarial tearing. Our new deep-learning approach uses a RAPTOR (residual, attention-based processing of tampered optical response) discriminator, showing the capability of identifying adversarial tampering to an optical, physical unclonable function based on randomly patterned arrays of gold nanoparticles. Using semantic segmentation and labeled clustering, we efficiently extract the positions and radii of the gold nanoparticles in the random patterns from 1000 dark-field images in just 27 ms and verify the authenticity of each pattern using RAPTOR in 80 ms with 97.6% accuracy under difficult adversarial tampering conditions. We demonstrate that RAPTOR outperforms the state-of-the-art Hausdorff, Procrustes, and average Hausdorff distance metrics, achieving a 40.6%, 37.3%, and 6.4% total accuracy increase, respectively.

Quantitative phase imaging (QPI) is a label-free technique that provides optical path length information for transparent specimens, finding utility in biology, materials science, and engineering. Here, we present QPI of a three-dimensional (3D) stack of phase-only objects using a wavelength-multiplexed diffractive optical processor. Utilizing multiple spatially engineered diffractive layers trained through deep learning, this diffractive processor can transform the phase distributions of multiple two-dimensional objects at various axial positions into intensity patterns, each encoded at a unique wavelength channel. These wavelength-multiplexed patterns are projected onto a single field of view at the output plane of the diffractive processor, enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor. Based on numerical simulations, we show that our diffractive processor could simultaneously achieve all-optical QPI across several distinct axial planes at the input by scanning the illumination wavelength. A proof-of-concept experiment with a 3D-fabricated diffractive processor further validates our approach, showcasing successful imaging of two distinct phase objects at different axial positions by scanning the illumination wavelength in the terahertz spectrum. Diffractive network-based multiplane QPI designs can open up new avenues for compact on-chip phase imaging and sensing devices.