Highly Reliable Synaptic Cell Array Based on Organic–Inorganic Hybrid Bilayer Stack toward Precise Offline Learning

Abstract

As the use of artificial intelligence (AI) soars, the development of novel neuromorphic computing is demanding because of the disadvantages of the von Neumann architecture. Furthermore, extensive research on electrochemical metallization (ECM) memristors as synaptic cells have been carried out toward a linear conductance update for online learning applications. In most cases, however, a conductance distribution change over time has not been studied as a major issue, giving less consideration to inference‐only computing accelerators based on offline learning. Herein, organic–inorganic bilayer stacking for synaptic unit cells using poly(1,3,5‐trivinyl‐1,3,5‐trimethyl cyclotrisiloxane) (pV3D3) and Al2O3 thin films is suggested, showing highly enhanced reliability for offline learning. The bilayer structure achieves better reliability and control of the analog resistive switching and synaptic functions, respectively, through the guided formation of conductive filaments via tip‐enhanced electric fields. In addition, 5‐bit multilevel states achieve long‐term stability (>104 s) following an in‐depth study on conductance‐level stability. Finally, a device‐to‐system‐level simulation is performed by building a convolutional neural network (CNN) based on the hybrid devices. This highlighted the significance of multilevel states in fully connected layers. It is believed that the study provides a practical approach to using ECM‐based memristors for inference‐only neural network accelerators.

Keywords

• 5-bit multilevel retention
• conductive-bridging random-access memory (CBRAM)
• fine-tuning
• neuromorphic computing
• offline learning