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
Link To Document :
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