Posted in | News | Quantum Computing

Advanced Terahertz Neural Network Unveiled at City University of Hong Kong

An innovative planar spoof plasmonic neural network (SPNN) platform capable of directly detecting and processing terahertz (THz) electromagnetic signals has been unveiled by researchers at City University of Hong Kong (CityUHK) and Southeast University in Nanjing.

The study has enormous potential for fields such as intelligent communication, advanced computing systems, and terahertz on-chip integration, all of which are crucial for the future of 6G.

The research project is led by Professor Chan Chi-hou, Chair Professor of the Department of Electrical Engineering and Director of State Key Laboratory of Terahertz and Millimeter Waves (SKLTMW) at CityUHK and Academician Cui Tiejun, Director of State Key Laboratory of Millimeter Waves, Southeast University.

The paper, “Terahertz spoof plasmonic neural network for diffractive information recognition and processing,” was recently published in Nature Communications.

The team set out to address the challenges posed by the rapid evolution of artificial intelligence. Traditional space-diffractive neural networks suffer from low-space transmission efficiency and large spatial dimensions, limiting their miniaturisation and broader applications. This new SPNN platform overcomes these limitations by offering a compact, efficient, and easily integrable solution.

The new technology, composed of compact spoof surface plasmon polaritons diffraction layers and phase-shifting layers, introduces a compact method for building and utilising neural networks. It can efficiently handle complex tasks like handwriting recognition and multi-user distinction, offering potential applications in terahertz on-chip integration and intelligent communication systems.

“The SPNN can directly process different users’ directions on the THz platform, integrating the capability of classifying handwritten digits without relying on digital processing,” said Dr Gao Xinxin, the first author of the paper and a postdoctoral fellow at SKLTMW.

The SPNN’s compactness, efficiency, and scalability make it an ideal candidate for artificial neural networks, addressing the power consumption and scalability issues of traditional digital computers. This network can directly process and recognise diffractive information with low power consumption and at the speed of light, broadening the application of terahertz plasmonic metamaterials.

“SKLTMW has excellent fabrication and measurement facilities supported by the Research Grants Council, the Innovation and Technology Commission of the HKSAR Government, and CityUHK,” said Professor Chan. “These facilities allow us to test our ideas promptly and generate unexpected results.”

Gu Ze and Dr Ma Qian, a PhD student and postdoctoral fellow, respectively, at Southeast University, are co-first authors of the paper. Other contributors are Cui Wenyi, PhD student, Professor You Jianwei of Southeast University, and Dr Chen Baojie and Dr Shum Kam-man of SKLTMW. Dr Ma, Academician Cui, and Professor Chan are the corresponding authors.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    City University of Hong Kong. (2024, November 11). Advanced Terahertz Neural Network Unveiled at City University of Hong Kong. AZoQuantum. Retrieved on November 13, 2024 from https://www.azoquantum.com/News.aspx?newsID=10605.

  • MLA

    City University of Hong Kong. "Advanced Terahertz Neural Network Unveiled at City University of Hong Kong". AZoQuantum. 13 November 2024. <https://www.azoquantum.com/News.aspx?newsID=10605>.

  • Chicago

    City University of Hong Kong. "Advanced Terahertz Neural Network Unveiled at City University of Hong Kong". AZoQuantum. https://www.azoquantum.com/News.aspx?newsID=10605. (accessed November 13, 2024).

  • Harvard

    City University of Hong Kong. 2024. Advanced Terahertz Neural Network Unveiled at City University of Hong Kong. AZoQuantum, viewed 13 November 2024, https://www.azoquantum.com/News.aspx?newsID=10605.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.