Efficient and compact integration of CMOS image sensors and cellular neural network (CNN) for intelligent processing

Chung-Yu Wu*, Wen Cheng Yen

*Corresponding author for this work

Research output: Contribution to conferencePaper

Abstract

By using the neuron-BJT as phototransistor and single-transistor neuron, the cellular neural network (CNN) can be compactly integrated with CMOS image sensors so that the optical images can be input to the CNN directly for neural image processing. With the neuron-BJT, realized by the parasitic pnp BJT in n-well CMOS technology, the optical-input CNN with symmetric templates can be implemented in very small chip area. The cell area can be as small as 20 um×24 um. The simulation results have confirmed the correct function of the proposed optical-input compact CNN.

Original languageEnglish
Pages232-236
Number of pages5
DOIs
StatePublished - 1 Dec 1999
EventProceedings of the 1999 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI'99 - Taipei, Taiwan
Duration: 15 Aug 199918 Aug 1999

Conference

ConferenceProceedings of the 1999 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI'99
CityTaipei, Taiwan
Period15/08/9918/08/99

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  • Cite this

    Wu, C-Y., & Yen, W. C. (1999). Efficient and compact integration of CMOS image sensors and cellular neural network (CNN) for intelligent processing. 232-236. Paper presented at Proceedings of the 1999 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI'99, Taipei, Taiwan, . https://doi.org/10.1109/MFI.1999.815995