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Inherent Stochastic Learning in CMOS-Integrated HfO2 Arrays for Neuromorphic Computing

Based on the inherent stochasticity of CMOS-integrated HfO 2 -based resistive random access memory (RRAM) devices, a new learning algorithm for neuro-morphic systems is presented. For this purpose, the device-to-device variability of CMOS-integrated 4-kbit 1T-1R arrays is examined. To demonstrate th...

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Bibliographic Details
Published in:IEEE electron device letters 2019-04, Vol.40 (4), p.639-642
Main Authors: Wenger, C., Zahari, F., Mahadevaiah, M. K., Perez, E., Beckers, I., Kohlstedt, H., Ziegler, M.
Format: Article
Language:English
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Summary:Based on the inherent stochasticity of CMOS-integrated HfO 2 -based resistive random access memory (RRAM) devices, a new learning algorithm for neuro-morphic systems is presented. For this purpose, the device-to-device variability of CMOS-integrated 4-kbit 1T-1R arrays is examined. To demonstrate the performance of the stochastic learning algorithm and the potential of RRAM technologies for neuro-morphic systems, a two-layer mixed-signal neural circuit for pattern recognition is implemented and tested with MNIST data.
ISSN:0741-3106
1558-0563
DOI:10.1109/LED.2019.2900867