A New Surface Potential and Physics Based Compact Model for a-IGZO TFTs at Multinanoscale for High Retention and Low-Power DRAM Application

作者：Jingrui Guo, Kaizhen Han, Subhali Subhechha, Xinlv Duan, Qian Chen, Di Geng, Shijie Huang, Lihua Xu, Junjie An, Gouri Sankar Kar, Xiao Gong, Lingfei Wang, Ling Li, Ming Liu · 年份：2021 · DOI：10.1109/iedm19574.2021.9720700 · 被引用次数：48 · 研究领域：Thin-Film Transistor Technologies、Image and Video Quality Assessment、Neural Networks and Reservoir Computing

Extremely scaled amorphous In-Ga-Zn-O thin film transistors (a-IGZO TFTs) can meet the increasing demand of high-density and low-power in DRAM design. Traditional percolation mechanism (PM) in a-IGZO TFTs compact model is done for infinite disordered systems, leading to error for scaled devices. To address this, a surface potential and physics-based compact model is proposed accounting for the effect of scaling on the device and material properties. Particularly, it is achieved with finite-size corrected hopping and percolation models derived using connected subnetworks. Multi-channel percolation (MCP) determines the performance for aggressively scaled device (e.g., sub-12nm) and exhibits a path-limited feature and power-law T-dependence. Final projections of current and capacitance characteristics are in excellent agreement with experiments, considering extreme-scaling induced severe short channel and contact effects. As the fabrication variability is significant in integrated circuits, we have explored statistical effects on subthreshold properties. Retention and statistical performances of a 2T0C configuration were subsequently evaluated and indicate a great potential for a-IGZO-based 3D-DRAM memories.