Fig. 5: Investigation of the impact of the read margin on classification accuracy. | Nature Communications

Fig. 5: Investigation of the impact of the read margin on classification accuracy.

From: Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators

Fig. 5

a DC I–V characteristics of the SRM-integrated CA, and (b) retention characteristics measured at a reading voltage of 1.7 V. Robust nonvolatility was observed at1.7 V. c Read current distributions of the LRS and HRS at 1.7 V. The coefficient of variations of the HRS and LRS (0.093 and 0.142, respectively) were relatively low. d Trained weight-mapping results obtained at a reading voltage of 1.7 V. e Comparison of the VMM operation results and calculated weight summations in each column. The feasibility of VMM operations using a 1.7 V reading voltage was confirmed. f Representative classification results of the CA under a 1.7 V reading voltage, and (g) total classification accuracy for each digit. All digits were classified accurately (1500 classification operations). Comparison of the two different reading margins (×50 @ 1.7 V and ×15 @ 2.0 V) revealed that the reading margin of the nonfilamentary-type memristor had an insignificant effect on the classification accuracy because of its highly uniform operating characteristics.

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