Vertically Stackable Ovonic Threshold Switch Oscillator Using Atomic Layer Deposited Ge0.6Se0.4 Film for High-Density Artificial Neural Networks,ACS Applied Materials & Interfaces

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Vertically Stackable Ovonic Threshold Switch Oscillator Using Atomic Layer Deposited Ge0.6Se0.4 Film for High-Density Artificial Neural Networks
ACS Applied Materials & Interfaces ( IF 8.3 ) Pub Date : 2024-03-16 , DOI: 10.1021/acsami.3c18625
Jeong Woo Jeon ₁ , Byongwoo Park ₁ , Yoon Ho Jang ₁ , Soo Hyung Lee ₁ , Sangmin Jeon ₁ , Janguk Han ₁ , Seung Kyu Ryoo ₁ , Kyung Do Kim ₁ , Sung Keun Shim ₁ , Sunwoo Cheong ₁ , Wonho Choi ₁ , Gwangsik Jeon ₁ , Sungjin Kim ₁ , Chanyoung Yoo ₂ , Joon-Kyu Han ₁ , Cheol Seong Hwang ₁

Affiliation

Nanodevice oscillators (nano-oscillators) have received considerable attention to implement in neuromorphic computing as hardware because they can significantly improve the device integration density and energy efficiency compared to complementary metal oxide semiconductor circuit-based oscillators. This work demonstrates vertically stackable nano-oscillators using an ovonic threshold switch (OTS) for high-density neuromorphic hardware. A vertically stackable Ge_0.6Se_0.4 OTS-oscillator (VOTS-OSC) is fabricated with a vertical crossbar array structure by growing Ge_0.6Se_0.4 film conformally on a contact hole structure using atomic layer deposition. The VOTS-OSC can be vertically integrated onto peripheral circuits without causing thermal damage because the fabrication temperature is <400 °C. The fabricated device exhibits oscillation characteristics, which can serve as leaky integrate-and-fire neurons in spiking neural networks (SNNs) and coupled oscillators in oscillatory neural networks (ONNs). For practical applications, pattern recognition and vertex coloring are demonstrated with SNNs and ONNs, respectively, using semiempirical simulations. This structure increases the oscillator integration density significantly, enabling complex tasks with a large number of oscillators. Moreover, it can enhance the computational speed of neural networks due to its rapid switching speed.

中文翻译：

采用原子层沉积 Ge0.6Se0.4 薄膜的垂直可堆叠 Ovonic 阈值开关振荡器，用于高密度人工神经网络

纳米器件振荡器（nano-oscillators）在神经形态计算中作为硬件实现受到了相当多的关注，因为与基于互补金属氧化物半导体电路的振荡器相比，它们可以显着提高器件集成密度和能源效率。这项工作演示了使用用于高密度神经形态硬件的 ovonic 阈值开关 (OTS) 的垂直堆叠纳米振荡器。通过使用原子层沉积在接触孔结构上保形生长Ge _0.6 Se _0.4薄膜，制备了具有垂直交叉阵列结构的垂直堆叠Ge _0.6 Se _0.4 OTS 振荡器（VOTS-OSC）。 VOTS-OSC 可以垂直集成到外围电路上，而不会造成热损坏，因为制造温度<400 °C。所制造的器件表现出振荡特性，可用作尖峰神经网络（SNN）中的泄漏积分和激发神经元以及振荡神经网络（ONN）中的耦合振荡器。对于实际应用，使用半经验模拟分别使用 SNN 和 ONN 演示了模式识别和顶点着色。这种结构显着提高了振荡器的集成密度，从而能够使用大量振荡器来完成复杂的任务。此外，由于其快速的切换速度，它可以提高神经网络的计算速度。

更新日期：2024-03-16

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