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a The complementary crossbar architecture [11, 12].
In this case, the twin crossbar shows better recognition rate than the complementary crossbar.
Figure 2a shows the complementary crossbar architecture which is composed of two memristor arrays of M+ and M− [11, 12].
Here, we compared the complementary crossbar architecture with the twin architecture for different variation and correlation parameters using the Monte Carlo method in Cadence Spectre software.
These two different points between the complementary crossbar and the twin one can affect the statistical-variation tolerance of the binary memristor array.
As indicated in Fig. 2a, the complementary crossbar has two M+ and M− arrays, where M− is the inversion of M+ array.
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This paper performs a comparative study on the statistical-variation tolerance between two crossbar architectures which are the complementary and twin architectures.
By summary, we can conclude that the twin crossbar is more robust than the complementary one under the same amounts of statistical variation and correlation.
One distinctive point of Fig. 2b from Fig. 2a is that the twin crossbar does not need to use the complementary M+ and M− arrays [9, 10].
To implement the crossbar circuit of pattern recognition, we can consider the other architecture different from the complementary one in Fig. 2a. Figure 2b shows the twin crossbar architecture with two identical M+ arrays of binary memristors [10].
On average, the twin crossbar shows better recognition rate by 4%% than the complementary one for the inter-array correlation = 1 and intra-array correlation = 0.
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