The group has actually established a world-leading MWP chip efficient in carrying out ultrafast analog electronic signal processing and calculation utilizing optics. Credit: City University of Hong Kong
A research study group has actually revealed a microwave photonic chip that considerably improves analog electronic signal processing, using 1,000 times the speed and higher energy performance than existing processors. This development guarantees to change different sectors, consisting of cordless interactions and expert system.
A research study group led by Professor Wang Cheng from the Department of Electrical Engineering (EE) at City University of Hong Kong (City UHK) has actually established a world-leading microwave photonic chip that can carrying out ultrafast analog electronic signal processing and calculation utilizing optics.
The chip, which is 1,000 times faster and takes in less energy than a standard electronic processor, has a vast array of applications, covering 5/6G cordless interaction systems, high-resolution radar systems, expert system, computer system vision, and image/video processing.
The group’s research study findings were released in the prominent clinical journal Nature entitled “Integrated Lithium Niobate Microwave Photonic Processing Engine.” It is a collective research study with The Chinese University of Hong Kong (CUHK).
Addressing Modern Communication Challenges
The fast growth of cordless networks, the Internet of Things, and cloud-based services has actually positioned substantial needs on underlying radio frequency systems. Microwave photonics (MWP) innovation, which utilizes optical parts for microwave signal generation, transmission, and control, provides reliable options to these difficulties. However, incorporated MWP systems have actually struggled to at the same time attain ultrahigh-speed analog signal processing with chip-scale combination, high fidelity, and low power.
“To address these challenges, our team developed a MWP system that combines ultrafast electro-optic (EO) conversion with low-loss, multifunctional signal processing on a single integrated chip, which has not been achieved before,” described Professor Wang.
Such efficiency is allowed by an incorporated MWP processing engine based upon a thin-film lithium niobate (LN) platform efficient in carrying out multi-purpose processing and calculation jobs of analog signals.
“The chip can perform high-speed analog computation with ultrabroad processing bandwidths of 67 GHz and excellent computation accuracies,” stated Feng Hanke, PhD trainee of EE and the very first author of the paper.
Pioneering Lithium Niobate Photonics
The group has actually been committed to looking into the incorporated LN photonic platform for a number of years. In 2018, coworkers at Harvard University and Nokia Bell laboratories established the world’s very first CMOS (complementary metal-oxide semiconductor)- suitable integrated electro-optic modulators on the LN platform, laying the structure for the present research study development. LN is described as the “silicon of photonics” for its significance to photonics, similar to silicon in microelectronics.
Their work opens a brand-new research study field, i.e., LN microwave photonics, making it possible for microwave photonics chips with compact sizes, high signal fidelity, and low latency; it likewise represents a chip-scale analog electronic processing and computing engine.
Reference: “Integrated lithium niobate microwave photonic processing engine” by Hanke Feng, Tong Ge, Xiaoqing Guo, Benshan Wang, Yiwen Zhang, Zhaoxi Chen, Sha Zhu, Ke Zhang, Wenzhao Sun, Chaoran Huang, Yixuan Yuan and Cheng Wang, 28 February 2024, Nature
DOI: 10.1038/ s41586-024-07078 -9
The paper’s very first authors are Feng Hanke and Ge Tong (EE undergrad). Professor Wang is the matching author. Other contributing authors consist ofDr Guo Xiaoqing, PhD graduate of EE;Dr Chen Zhaoxi,Dr Zhang Ke,Dr Zhu Sha (likewise at Beijing University of Technology),Dr Sun Wenzhao (now at City UHK (Dongguan)), EE postdocs; and Zhang Yiwen, EE PhD trainee; and partners (Wang Benshan, Professor Huang Chaoran, and Professor Yuan Yixuan) from CUHK.
