Ultralow cyclic and device variability memristors via deterministic resistive switching in phase segregated nickel nanofilaments

Abstract

Memristor crossbar arrays, mimicking the human brain, hold immense potential for energy-efficient data-intensive computations in artificial intelligence applications such as image recognition and natural language processing. However, the stochastic nature of resistive switching (RS) in memristors often leads to poor device stability and uniformity, hindering the scalability required for real-world applications. Here, we present a novel phase segregation strategy to achieve uniformly distributed self-assembled Ni nanofilaments within a BaTiO3 matrix, enabling local deterministic redox reactions for RS, as confirmed by comprehensive structural, local, and macroscopic RS studies. This approach yields drastic enhancement in cyclic performance and device uniformity, with the average cyclic variances of Set voltage and low resistance state down to 1.4% and 9.6%, respectively. The devices also exhibit excellent endurance (109 cycles) and ultrafast programming speed (down to 100 ns) and achieve over 5-bit level long-term memory states. The enhanced cyclic stability and device uniformity translate to high training and learning accuracies (95%) in a three-level deep neural network, with 1-bit inputs. Our phase segregation strategy provides a generic pathway to overcome the long-standing challenge of device variability in neuromorphic computing.