• DocumentCode
    1553502
  • Title

    An ART1 microchip and its use in multi-ART1 systems

  • Author

    Serrano-Gotarrdeona, T. ; Linares-Barranco, Bernabé

  • Author_Institution
    Nat. Microelectron. Center, Seville, Spain
  • Volume
    8
  • Issue
    5
  • fYear
    1997
  • fDate
    9/1/1997 12:00:00 AM
  • Firstpage
    1184
  • Lastpage
    1194
  • Abstract
    Recently, a real-time clustering microchip neural engine based on the ART1 architecture has been reported. However, that chip rendered an extremely high silicon area consumption of 1 cm2, and consequently an extremely low yield of 6%. Redundant circuit techniques can be introduced to improve yield performance at the cost of further increasing chip size. In this paper we present an improved ART1 chip prototype based on a different approach to implement the most area consuming circuit elements of the first prototype: an array of several thousand current sources which have to match within a precision of around 1%. Such achievement was possible after a careful transistor mismatch characterization of the fabrication process (ES2-1.0 μm CMOS). A new prototype chip has been fabricated which can cluster 50-b input patterns into up to ten categories. The chip has 15 times less area, shows a yield performance of 98%, and presents the same precision and speed than the previous prototype. Due to its higher robustness multichip systems are easily assembled. As a demonstration we show results of a two-chip ART1 system, and of an ARTMAP system made of two ART1 chips and an extra interfacing chip
  • Keywords
    ART neural nets; CMOS analogue integrated circuits; CMOS memory circuits; VLSI; analogue processing circuits; integrated circuit design; learning systems; multichip modules; neural chips; real-time systems; ART1 neural chip; ARTMAP system; CMOS IC; CMOS memory IC; IC design; VLSI; analog IC; analog processing circuit; clustering method; learning systems; multichip systems; multiple ART1 systems; transistor mismatch characterization; Application software; Assembly systems; Circuits; Clustering algorithms; Costs; Engines; Hardware; Prototypes; Real time systems; Robustness;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.623219
  • Filename
    623219