Hybrid Nanoprinted Neural Networks

• doi: 10.1109/SUM60964.2024.10614502
• title: Hybrid Nanoprinted Neural Networks
• publisher: IEEE
• isbn: 979-8-3503-9388-0
• issn: 1099-4742
• rank: 2671
• access_type: LOCKED
• content_type: Conferences
• abstract: Diffractive neural networks (DNNs) exploit the nature of light propagation in free space and the interaction of a light field with metasurfaces designed with machine learning methods to implement inference passively in the optical domain1. In this way, DNNs can achieve all-optical machine learning tasks such as image classification or decryption2. When nanoprinted with two photon nanolithography (TPN) methods, DNNs can express their full potential achieving a neuron density of >500 million neurons per square centimeter, operative wavelength in the near infrared or visible wavelength regime and on-chip integration with imaging sensors, such as commercial complementary metal-oxide-semiconductor (CMOS) sensors3.
• article_number: 10614502
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614502
• html_url: https://ieeexplore.ieee.org/document/10614502/
• abstract_url: https://ieeexplore.ieee.org/document/10614502/
• publication_title: 2024 IEEE Photonics Society Summer Topicals Meeting Series (SUM)
• conference_location: Bridgetown, Barbados
• conference_dates: 15-17 July 2024
• publication_number: 10614447
• is_number: 10614479
• publication_year: 2024
• publication_date: 15-17 July 2024
• start_page: 1
• end_page: 1
• citing_paper_count: 0
• citing_patent_count: 0
• download_count: 61
• insert_date: 20240806
• index_terms:
  • ieee_terms:
    • Neural networks
    • Photonics
  • dynamic_index_terms:
    • Neural Network
    • Image Classification
    • Free Space
    • Digital Networks
    • Computer Network
    • Image Sensor
    • Camera Sensor
    • Metasurface
    • Optical Domain
    • Free-space Propagation
    • Computational Complexity Reduction
    • Diffractive Elements
    • Hybrid Neural Network
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 979-8-3503-9388-0, isbnType: New-2005
  • format: Electronic ISBN, value: 979-8-3503-9387-3, isbnType: New-2005
• authors:
  • Author Name: Elena Goi
    Affiliation: School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology, Shanghai, PR, China
    Author URL: https://ieeexplore.ieee.org/author/37085507583
    ID: 37085507583
    Order: 1
    Author Affiliations:
    • School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology, Shanghai, PR, China
    • Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, PR, China

Image Sensor

• sensor_type: CMOS
• resolution: Not specified
• dynamic_range: Not specified
• pixel_size: Not specified
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

Applications & Benefits

• cell_imaging: Not specified
• benefits: Not specified

Supporting Organizations

• supported_by: Not specified

Manuscript Details

• publication_date: 15-17 July 2024

Relevancy Score

• score: 5
• missing_fields:
  • resolution
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • signal_to_noise_ratio
  • sensitivity
  • shutter_speed
  • power_consumption
  • noise
  • cell_imaging
  • benefits
  • supported_by

Processed JSON Filename

request_c0aa5908-6647-40ea-8bcc-f2a239b97240-hybrid_nanoprinted_neural_networks.json