Investigation of ReRAM Variability on Flow-Based Edge Detection Computing Using HfO₂-Based ReRAM Arrays

Resistive random-access memory (ReRAM) memristors are promising candidates for various compute in memory and flow-based computing approaches. As an alternative to traditional von Neumann computation, flow-based computing avoids serial movement of data between memory and processor. In this paper, we demonstrate arrays of 1 transistor 1 ReRAM (1T1R) to detect edges between 8 bit pixels using flow-based computing, and the effects of stochastic variation of ReRAM on edge detection outputs. Three different \text{R}_{\mathrm {off}}/\text{R}_{\mathrm {on}} resistance ratios (1.5:1, 2.5:1 or 28.6:1) were utilized to implement multiple flow-based edge detection computation matrices for 8 bit pixels. Edge detection was distinguishable for all \text{R}_{\mathrm {off}}/\text{R}_{\mathrm {on}} ratios used, for all flow-based computing matrices. However, the binary output resistance ratio of the matrices improved 3-fold when the patterned \text{R}_{\mathrm {off}}/\text{R}_{\mathrm {on}} ratio was increased to 28.6:1. A Gaussian simulation of ReRAM resistance variability validates the experimental data, with a correlation coefficient (r) of 0.9547. These results suggest a trade-off between the flow-based edge detection output ratio and the variability of the ReRAM resistance in \text{R}_{\mathrm {off}}/\text{R}_{\mathrm {on}} resistance ratio.