Super-Linear-Threshold-Switching Selector with Multiple Jar-Shaped Cu-Filaments in the Amorphous Ge3Se7 Resistive Switching Layer in a Cross-Point Synaptic Memristor Array,Advanced Materials

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Super-Linear-Threshold-Switching Selector with Multiple Jar-Shaped Cu-Filaments in the Amorphous Ge3Se7 Resistive Switching Layer in a Cross-Point Synaptic Memristor Array
Advanced Materials ( IF 27.4 ) Pub Date : 2022-08-18 , DOI: 10.1002/adma.202203643
Hea-Jee Kim ₁ , Dae-Seong Woo ₁ , Soo-Min Jin ₁ , Hyo-Jun Kwon ₂ , Ki-Hyun Kwon ₃ , Dong-Won Kim ₃ , Dong-Hyun Park ₂ , Dong-Eon Kim ₂ , Hong-Uk Jin ₁ , Hyun-Do Choi ₂ , Tae-Hun Shim ₄ , Jea-Gun Park _{1,

2,

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Affiliation

The learning and inference efficiencies of an artificial neural network represented by a cross-point synaptic memristor array can be achieved using a selector, with high selectivity (I_on/I_off) and sufficient death region, stacked vertically on a synaptic memristor. This can prevent a sneak current in the memristor array. A selector with multiple jar-shaped conductive Cu filaments in the resistive switching layer is precisely fabricated by designing the Cu ion concentration depth profile of the CuGeSe layer as a filament source, TiN diffusion barrier layer, and Ge₃Se₇ switching layer. The selector performs super-linear-threshold-switching with a selectivity of > 10⁷, death region of −0.70–0.65 V, holding time of 300 ns, switching speed of 25 ns, and endurance cycle of > 10⁶. In addition, the mechanism of switching is proven by the formation of conductive Cu filaments between the CuGeSe and Ge₃Se₇ layers under a positive bias on the top Pt electrode and an automatic rupture of the filaments after the holding time. Particularly, a spiking deep neural network using the designed one-selector-one-memory cross-point array improves the Modified National Institute of Standards and Technology classification accuracy by ≈3.8% by eliminating the sneak current in the cross-point array during the inference process.

中文翻译：

在交叉点突触忆阻器阵列中的非晶 Ge3Se7 电阻开关层中具有多个罐形铜丝的超线性阈值开关选择器

以交叉点突触忆阻器阵列为代表的人工神经网络的学习和推理效率可以使用选择器来实现，该选择器具有高选择性（I _on / I _off）和足够的死亡区域，垂直堆叠在突触忆阻器上。这可以防止忆阻器阵列中的潜行电流。_{通过设计CuGeSe层作为灯丝源、TiN扩散阻挡层和Ge 3} Se ₇开关层的Cu离子浓度深度分布，精确制作了电阻开关层中具有多个罐形导电Cu细丝的选择器。^{选择器以 > 10 7}的选择性执行超线性阈值切换，-0.70-0.65 V 的死区，300 ns 的保持时间，25 ns 的开关速度，以及 > 10 ⁶的持久循环。此外，通过在顶部 Pt 电极上的正偏压下在 CuGeSe 和 Ge ₃ Se ₇层之间形成导电 Cu 细丝以及在保持时间后细丝自动断裂来证明切换机制。特别是，使用设计的单选择器一内存交叉点阵列的脉冲深度神经网络通过消除推理过程中交叉点阵列中的潜行电流，将修改后的美国国家标准与技术研究院分类精度提高了约 3.8%过程。

更新日期：2022-08-18

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