Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System.

In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors.

Molecular-Shape-Controlled Binary to Ternary Resistive Random-Access Memory Switching of N-Containing Heteroaromatic Semiconductors.

In organic resistive random-access memory (ReRAM) devices, deeply understanding how to control the performance of π-conjugated semiconductors through molecular-shape-engineering is important and highly desirable. Herein, we design a family of N-containing heteroaromatic semiconductors with molecular shapes moving from mono-branched 1Q to di-branched 2Q and tri-branched 3Q. We find that this molecular-shape engineering can induce reliable binary to ternary ReRAM switching, affording a highly enhanced device yield that satisfies the practical requirement.