The University of California, Los Angeles (UCLA) has made significant strides in the realm of optical information transmission. Led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department, a team of researchers has developed a new method that addresses the challenges posed by phase perturbations or random diffusers in the optical path. These obstacles have traditionally limited the high-fidelity transmission of optical data in free space, a phenomenon critical to various applications such as underwater communication, remote sensing, and medical devices.
The UCLA team’s breakthrough, detailed in an article published in Advanced Photonics, introduces an innovative approach that employs electronic encoding and diffractive optical decoding. This method allows for the high-fidelity transmission of optical information through random, unknown diffusers. The technique outperforms systems that rely solely on either a diffractive optical network or an electronic neural network for optical information transfer through diffusive random media.
The new approach hinges on a hybrid model that incorporates a convolutional neural network (CNN)-based electronic encoder and co-optimized transmissive passive diffractive layers. These components are physically fabricated and trained via supervised learning. Once the joint training process is completed, the hybrid model can accurately transfer optical information, even in the presence of unknown phase diffusers. This capability allows it to generalize and pass information through unseen random diffusers.
The effectiveness of this hybrid electronic-optical model was verified using a 3D-printed diffractive network operating in the terahertz part of the electromagnetic spectrum. Another notable feature of this model is its scalability. The optical decoder can be physically expanded or shrunk to operate across different parts of the electromagnetic spectrum, eliminating the need for retraining its diffractive features.
This technological breakthrough holds significant implications for the electronics and computer industry, particularly in areas involving programming languages and coding. The ability to transmit optical information through random diffusers with high fidelity can enhance data transmission quality and speed in various applications. For instance, it could improve underwater optical communication, a critical aspect of marine electronics.
Moreover, this development could revolutionize biomedical sensing and imaging data transmission in implantable systems. Medical devices often require intricate electronics and sophisticated coding to function efficiently. By providing a low-power and compact alternative for data transmission, this hybrid model could significantly improve these devices’ performance and reliability.
Additionally, this technology could also be instrumental in data transmission through turbulent atmospheric conditions, a challenge often encountered in remote sensing applications. By ensuring high-fidelity transmission despite unpredictable phase perturbations, this method could enhance the accuracy and reliability of data collected through remote sensing.
In conclusion, the UCLA team’s innovative approach to transferring optical information represents a significant leap forward in the field of electronics and computers. By combining electronic encoding with diffractive optical decoding, this method offers a promising solution to the challenges posed by phase perturbations and random diffusers. This development has far-reaching implications for various applications, from underwater communication and remote sensing to medical devices.
For more details about this groundbreaking research, refer to the Gold Open Access article by Li et al., “Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding,” Adv. Photon. 4(4) 046009 (2023), doi 10.1117/1.AP.5.4.046009.
