Tuning the analog synaptic properties of forming free SiO2 memristors by material engineering

Applied Physics Letters, Volume 118, Issue 14, April 2021. The development of alternative brain-inspired neuromorphic computing architectures is anticipated to play a key role in addressing the strict requirements of the artificial intelligence era. In order to obtain a high degree of learning accuracy within an artificial neural network (ANN) that operates with the backpropagation algorithm, a highly symmetric synaptic weight distribution is desired. Along these lines, we present here a detailed device engineering approach that enables analog synaptic properties in completely forming free SiO2-conductive bridge memories. This is achieved by either incorporating a dense layer of Pt nanoparticles as a bottom electrode or fabricating bilayer structures using a second switching layer of VOx. Interestingly, compared with the reference sample that manifests both threshold and bipolar switching modes, the Pt NC sample exhibits only the threshold switching pattern, whereas the bilayer configuration operates only under the bipolar switching mode, as illustrated by direct current measurements. These characteristics have a direct, while different impact, on the conductance modulation pattern and determine the analog nature of the synaptic weight distribution. Valuable insights regarding the origin of these effects and, in particular, of the symmetric and linear conductance modulation processes are gained through the implementation of a self-consistent numerical model that takes into account both the impact of the electrodes' thermal conductivity on the switching pattern and the different diffusion barriers for silver ion migration. Our approach provides useful guidelines toward the realization of high yield ANNs with biological-like dynamic behavior by controlling the conducting filament growth mechanism.