Power-Delay Area-Efficient Processing-In-Memory Based on Nanocrystalline Hafnia Ferroelectric Field-Effect Transistors

被引:11
|
作者
Kim, Giuk [1 ]
Ko, Dong Han [2 ]
Kim, Taeho [1 ]
Lee, Sangho [1 ]
Jung, Minhyun [1 ]
Lee, Young Kyu [2 ]
Lim, Sehee [2 ]
Jo, Minyoung [2 ]
Eom, Taehyong [1 ]
Shin, Hunbeom [1 ]
Jeong, Yeongseok [1 ]
Jung, Seongook [2 ]
Jeon, Sanghun [1 ]
机构
[1] Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon 34141, South Korea
[2] Yonsei Univ, Sch Elect Engn, Seoul 03722, South Korea
关键词
processing in-memory; energy efficiency; area efficiency; memory window; endurance; FeFETs; MULTIBIT INPUT; MACRO; SRAM; COMPUTATION; WEIGHT; ENERGY; ARRAY;
D O I
10.1021/acsami.2c14867
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Ferroelectric field-effect transistors (FeFETs) have attracted enormous attention for low-power and high-density nonvolatile memory devices in processing-inmemory (PIM). However, their small memory window (MW) and limited endurance severely degrade the area efficiency and reliability of PIM devices. Herein, we overcome such challenges using key approaches covering from the material to the device and array architecture. High ferroelectricity was successfully demonstrated considering the thermodynamics and kinetics, even in a relatively thick (>= 30 nm) ferroelectric material that was unexplored so far. Moreover, we employed a metal-ferroelectric-metal-insulator-semiconductor architecture that enabled desirable voltage division between the ferroelectric and the metal-oxide-semiconductor FET, leading to a large MW (similar to 11 V), fast operation speed (<20 ns), and high endurance (similar to 10(11) cycles) characteristics. Subsequently, reliable and energy-efficient multiply-and-accumulation (MAC) operations were verified using a fabricated FeFET-PIM array. Furthermore, a system-level simulation demonstrated the high energy efficiency of the FeFET-PIM array, which was attributed to charge-domain computing. Finally, the proposed signed weight MAC computation achieved high accuracy on the CIFAR-10 dataset using the VGG-8 network.
引用
收藏
页码:1463 / 1474
页数:12
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