Devices that exhibit resistive switching are promising components for future nanoelectronics with applications ranging from emerging memory to neuromorphic computing and biosensors. In this brief, we present an algorithm for identifying switchable devices, i.e., devices that can be programmed in distinct resistive states and that change their state predictably and repeatedly in response to input stimuli. The method is based on extrapolating the statistical significance of difference in between two distinct resistive states as measured from devices subjected to standardized bias protocols. The test routine is applied on distinct elements of 32    $\times$  32 resistive-random-access-memory (RRAM) crossbar arrays and yields a measure of device switchability in the form of a statistical significance    $p$  -value. Ranking devices by    $p$  -value shows that switchable devices are typically found in the bottom 10% and are therefore easily distinguishable from nonfunctional devices. Implementation of this algorithm dramatically cuts RRAM testing time by granting fast access to the best devices in each array, as well as yield metrics.
['Isha Gupta', 'Alexantrou Serb', 'Radu Berdan', 'Ali Khiat', 'Anna Regoutz', 'T. Prodromakis']
A Cell Classifier for RRAM Process Development