Passive LiNbO₃ Memristor With Multilevel States for Neuromorphic Computing

The implementation of multilevel conductance states is still difficult for passive memristors used in neuromorphic computing. Here, a passive single-crystalline LiNbO 3 (LN) memristor with multilevel states was proposed, which can be precisely programmed into multilevel target states (with a standard deviation below 0.008~\mu s). Moreover, 32 separated and reliable conductance states can be achieved. The pattern recognition simulation for different numbers of conductance states ( {N}_{\textrm {G}} ) is performed. As {N}_{\textrm {G}} increases, the inference accuracy rises and reaches 98.01% when {N}_{\textrm {G}} is 32. Even taking into account the conductance programming and drift error of the memristors, the accuracy can still reach 90.37%. The results validate the application potential of this passive memristor with 32 conductance states in neuromorphic computing.