Neuralbased Smart Cmos Sensors For Online Pattern Classification Applications

• doi: 10.1109/ISCAS.1996.541982
• title: Neural-based smart CMOS sensors for on-line pattern classification applications
• publisher: IEEE
• isbn: 0-7803-3073-0
• issn:
• partnum: 96CH35876
• rank: 3097
• access_type: LOCKED
• content_type: Conferences
• abstract: We review previous work on CMOS photoreceptors and neural-based smart sensors that are VLSI realization of a neural network classifier with an integrated photoreceptor array. These sensors are designed for on-line pattern classification applications requiring image capture or non-contact measurement. Photoreceptors are based on Field-Effect-Modified parasitic phototransistors in CMOS technology. Several designs have been implemented. A pre-programmed smart photosensor fabricated in 3 /spl mu/ CMOS has been successfully tested and a programmable version has been fabricated in 1.2 /spl mu/. The latest design of smart sensor is based on a novel unified synapse-neuron building block that results in a highly modular, scalable and area-efficient VLSI architecture. A test circuit containing an 8/spl times/8 photosensitive array and a fully-connected programmable neural network with N=54 inputs, m=8 hidden neurons and k=4 output neurons has been designed in the 1.2 /spl mu/ technology. Based on the new architecture synaptic density has been doubled, and we have been able to increase the size of the optical input array as well as neural classifier itself.
• article_number: 541982
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=541982
• html_url: https://ieeexplore.ieee.org/document/541982/
• abstract_url: https://ieeexplore.ieee.org/document/541982/
• publication_title: 1996 IEEE International Symposium on Circuits and Systems (ISCAS)
• conference_location: Atlanta, GA, USA
• conference_dates: 15-15 May 1996
• publication_number: 3834
• is_number: 11198
• publication_year: 1996
• publication_date: 15-15 May 1996
• start_page: 384
• end_page: 387 vol.4
• citing_paper_count: 2
• citing_patent_count: 0
• download_count: 98
• insert_date: 20020806
• index_terms:
  • ieee_terms:
    • Intelligent sensors
    • Pattern classification
    • CMOS image sensors
    • Photoreceptors
    • Circuit testing
    • Very large scale integration
    • Neural networks
    • Sensor arrays
    • CMOS technology
    • Neurons
  • dynamic_index_terms:
    • Neural Network
    • Building Blocks
    • Output Neurons
    • Neural Network Classifier
    • Hidden Neurons
    • Online Application
    • Sensor Design
    • Modular Architecture
    • Synaptic Density
    • CMOS Technology
    • Neural Classifier
    • Smart Sensors
    • Non-contact Measurement
    • Scalable Architecture
    • Online Classification
    • Input Array
    • Synapse
    • Presynaptic
    • Signal Processing
    • Output Layer
    • Synaptic Weights
    • Neurons In The Hidden Layer
    • Neurons In Each Hidden Layer
    • Hidden Layer
    • Photoreceptor Cells
    • Photoreception
    • Random Access
    • Photocurrent
    • State Of Neurons
    • Neurons In Conditions
    • Neurons In Layer
    • Imaging Applications
    • Memory Regions
• isbn_formats:
  • format: Print ISBN, value: 0-7803-3073-0, isbnType: Historical
• authors:
  • Author Name: H. Djahanshahi
    Affiliation: Department of Electrical Engineering, University of Windsor, Windsor, ONT, Canada
    Author URL: https://ieeexplore.ieee.org/author/37354877100
    ID: 37354877100
    Order: 1
    Author Affiliations:
    • Department of Electrical Engineering, University of Windsor, Windsor, ONT, Canada
  • Author Name: G.A. Jullien
    Affiliation: Department of Electrical Engineering, University of Windsor, Windsor, ONT, Canada
    Author URL: https://ieeexplore.ieee.org/author/37276214100
    ID: 37276214100
    Order: 2
    Author Affiliations:
    • Department of Electrical Engineering, University of Windsor, Windsor, ONT, Canada
  • Author Name: W.C. Miller
    Affiliation: Department of Electrical Engineering, University of Windsor, Windsor, ONT, Canada
    Author URL: https://ieeexplore.ieee.org/author/37269502100
    ID: 37269502100
    Order: 3
    Author Affiliations:
    • Department of Electrical Engineering, University of Windsor, Windsor, ONT, Canada
  • Author Name: L. Ahmadi
    Affiliation: Department of Electrical Engineering, University of Windsor, Windsor, ONT, Canada
    Author URL: https://ieeexplore.ieee.org/author/37087760929
    ID: 37087760929
    Order: 4
    Author Affiliations:
    • Department of Electrical Engineering, University of Windsor, Windsor, ONT, Canada

Image Sensor

• sensor_type: CMOS
• resolution: 8x8
• 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: Used for low-resolution pattern classification applications in process control
• benefits: Allows for integration of sensor and signal processing circuitry in one chip, improving efficiency and reducing costs.

Supporting Organizations

• supported_by: NSERC, Micronet, Ortho-McNeil Inc., Canadian Microelectronics Corporation

Manuscript Details

• publication_date: 15-15 May 1996

Relevancy Score

• score: 10
• missing_fields:
  • dynamic_range
  • signal_to_noise_ratio
  • sensitivity
  • shutter_speed
  • power_consumption
  • frame_rate

Processed JSON Filename

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