LAUSR

Abstract The limitation of traditional Von Neumann architecture could be resolved by machine learning training in neuromorphic computing. However, the nonlinearity characteristic during conductance modulation in memristor severely restricts its application in neuromorphic computing. To improve the analog switching including linearity of the hafnium oxide memristors, Ti metal layer has been inserted on hafnium oxide by using the redox reaction on the interface to achieve more gradual switching. Furthermore, a new multilayer structure device is fabricated utilizing the Ti/HfOx interface characteristic, which enhanced the electrical characteristic and the conductance modulation linearity to 98.4%, amplitude and symmetry have also been improved. The conductive mechanism of segmented growth of conductive filaments by adjusting the oxygen vacancy concentration gradient was analyzed and further characterized by XPS and TEM. The results of this paper will exalt conductance modulation linearity of hafnium oxide memristors for application to neuromorphic computing systems.