Publication

Variability-Aware Design of Multilevel Logic Decoders for Nanoscale Crossbar Memories

Abstract

The fabrication of crossbar memories with sublithographic features is expected to be feasible within several emerging technologies; in all of them, the nanowire (NW) decoder is a critical part since it bridges the sublithographic wires to the outer circuitry that is defined on the lithography scale. In this paper, we evaluate the addressing scheme of the decoder circuit for NW crossbar arrays, based on the existing technological solutions for threshold voltage differentiation of NW devices. This is equivalent to using a multivalued logic addressing scheme. With this approach, it is possible to reduce the decoder size and keep it defect tolerant. We formally define two types of multivalued codes (i.e., hot and reflexive codes), and we estimate their yield under high variability conditions. Multivalued hot decoders yield better area saving than n-ary reflexive codes, and under severe conditions, reflexive codes enable a nonvanishing part of the code space to randomly recover. The choice of the optimal combination of decoder type and logic level saves area up to 24%. We also show that the precision of the addressing voltages when a high variability affects the threshold voltages is a crucial parameter for the decoder design and permits large savings in memory area. Moreover, a precise knowledge about the variability level improves the design of memory decoders by giving the right optimal code.

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A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example, the Fano algorithm). The Viterbi algorithm is the most resource-consuming, but it does the maximum likelihood decoding. It is most often used for decoding convolutional codes with constraint lengths k≤3, but values up to k=15 are used in practice. Viterbi decoding was developed by Andrew J.
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