近日，苏州大学Mario Lanza课题组在顶级期刊杂志《Nature Electronics》上发表了题为“Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural n ...
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文章链接：https://doi.org/10.1038/s41928-020-00473-w 背景 在固态电子器件和电路中应用二维材料，有助于扩展摩尔定律，并能获得优于CMOS的现先进产品。此前已有过一些二维材料的晶圆集成的报道，但其大部分的电路应用相对简单（例如逻辑门和传感器），并且以来与大尺寸的器件（例如沟道距离大于100 μm的晶体管），但这些器件是孤立的，且没有给出它们的应用 摘要 二维材料可以在后CMOS(互补金属-氧化物-半导体)时代的电子器件中发挥重要作用，利用这种材料开发用于信息存储和神经形态计算的忆阻器是特别值得关注的。然而，由于器件产率低和器件对器件的高变异性，用于复杂应用的高密度电子电路的制造受到了限制。本文研究了用六方的氮化硼作为电阻开关材料可以制作高密度的记忆性交叉栅阵列，并将其用于建立人工神经网络来进行图像识别。采用化学气相沉积法沉积多层六方氮化硼，阵列表现出高产率(98%)、低循环/循环变异性(1.53%)和低设备/设备变异性(5.74%)。根据所使用的电极材料(金用于双极开关，银用于阈值开关)以及特性(如大动态范围和zepto焦耳阶开关能量)，这些器件表现出不同的开关机制，使它们适合应用于神经形态电路。 Two-dimensional materials could play an important role in beyond-CMOS (complementary metal–oxide–semiconductor) electronics, and the development of memristors for information storage and neuromorphic computing using such materials is of particular interest. However, the creation of high-density electronic circuits for complex applications is limited due to low device yield and high device-to-device variability. Here, we show that high-density memristive crossbar arrays can be fabricated using hexagonal boron nitride as the resistive switching material, and used to model an artificial neural network for image recognition. The multilayer hexagonal boron nitride is deposited using chemical vapour deposition, and the arrays exhibit a high yield (98%), low cycle-to-cycle variability (1.53%) and low device-to-device variability (5.74%). The devices exhibit different switching mechanisms depending on the electrode material used (gold for bipolar switching and silver for threshold switching), as well as characteristics (such as large dynamic range and zeptojoule-order switching energies) that make them suited for application in neuromorphic circuits. |
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