Error compensated MOF-based ReRAM array for encrypted logical operations

Abstract

Metal-organic frameworks (MOFs) form a unique platform for operating with data using ReRAM technology. Here we report on a large-scale fabrication of MOF-based ReRAM array with 6x6 cells, demonstrating 50 % variation of their electronic parameters. Despite this inhomogeneity, such "non-ideal" ReRAM array is used for recording binary information followed by deep learning processes to achieve 95 % of accuracy of reading. Next, the same ReRAM array is used to record the numbers (from 0 to 15) followed by the operation of addition. For the correct performance of such analogous algorithm, we determine a set of unique coefficients for each ReRAM cell, allowing us to use this set than as an encrypted key to get an access to logical operations. The obtained results, thereby, demonstrate the possibility of "non-ideal" MOF-based ReRAM for low error reading of information coupled with an encrypted logical operations.