An Efficient Design of TaOx‐Based Memristor by Inserting an Ultrathin Al2O3 Layer with High Stability for Neuromorphic Computing and Logic Operation

Abstract

Abstract New computing‐in‐memory architecture based on memristors can achieve in situ storage and computing of data, which greatly improves the computing efficiency of the hardware system. Here, a reliable bilayer structured TaOx/Al2O3 memristor with a 2 nm Al2O3 insertion layer is demonstrated. This device exhibits stable and gradual switching behavior with a low set/reset voltage (0.61 V/−0.49 V) and multilevel conductance characteristics. It is further indicated that the device has a larger ON/Off ratio (≈148×) and better nonlinearity of conductance modulation by inserting an Al2O3 layer. Various forms of synaptic plasticity are mimicked, such as long‐term potentiation/depression (LTP/LTD), paired‐pulse facilitation (PPF), and spike‐timing‐dependent plasticity (STDP). Based on the quasi‐linear conductance modulation characteristics, excellent classification accuracy (90.4%) is achieved for the applications of handwritten digit recognition. Moreover, the logic operations (intersection, union, and complement) are implemented on a 3 × 5 memristor array, which shows an efficient way to design versatile and reliable devices and provides a novel idea for neuromorphic computing and in‐memory logic operation.

Keywords

• logic operation
• memristors
• neuromorphic computing
• synaptic plasticity