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Implementing hardware primitives based on memristive spatiotemporal variability into cryptography applications
Chip,
2023, 2(1):
100040-12.
DOI: 10.1016/j.chip.2023.100040
Fig. 3. Artificial intelligence-based analysis of DDV and CCV. a, Principal Component Analysis of DDV. b1, Schematic illustration of long short-term memory cell of cycle i and cycle i+1, where the physical parameter of cycle i (i ranges from 1 to 255) is extracted from IV curves like b2, including HRS current, current before Set, Set voltage, LRS current, Reset voltage/current and current/voltage after Reset. c-j, The distribution of the Set voltage, voltage after Reset, LRS current, current after Reset, Reset current, HRS current, current before Set, and Reset voltage for 256 cycles, where the grey dots indicate voltage values and the blue dots indicate current values. k-l. The SHAP value and the mean value of the SHAP of the contribution of all the physical parameters to the Set voltages.
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