Forming-Less Compliance-Free Multistate Memristors as Synaptic Connections for Brain-Inspired Computing,ACS Applied Electronic Materials

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Forming-Less Compliance-Free Multistate Memristors as Synaptic Connections for Brain-Inspired Computing
ACS Applied Electronic Materials ( IF 4.3 ) Pub Date : 2020-02-25 , DOI: 10.1021/acsaelm.0c00002
Sien Ng ₁ , Rohit Abraham John ₁ , Jing-ting Yang ₁ , Nripan Mathews _{1,

2}

Affiliation

Hardware realization of artificial neural networks (ANNs) requires analogue weights to be encoded into the device conductances via blind update and access operations, leveraging Kirchhoff’s circuit laws. However, most memristive solutions lag behind in this aspect due to numerous device nonidealities, like limited number of addressable states, need for a stringent compliance current control, and an electroforming process. By modulating the oxygen vacancy profile of tin oxide switching elements, here we design and evaluate multistate memristors as synaptic connections for brain-inspired computing. Harnessing the advantages of a forming-less compliance-free operation, our devices display gradual switching transitions across multiple conductance states, sufficing the switching requirements of synaptic connections in an ANN. The soft boundary conditions are analyzed systematically, and spike-based plasticity rules, state-dependent spike-timing-dependent-plasticity (STDP) modulations, ternary digital logic, and analogue updatability schemes are proposed and demonstrated comprehensively to establish the analogue programming window of our memristors.

中文翻译：

形成较少合规性的多状态忆阻器，作为用于大脑启发式计算的突触连接

人工神经网络（ANN）的硬件实现要求利用基尔霍夫的电路定律，通过盲更新和访问操作将模拟权重编码到设备电导中。但是，由于许多器件的非理想性，例如有限数量的可寻址状态，需要严格的顺应性电流控制和电铸工艺，因此大多数忆阻解决方案在这方面落后。通过调节氧化锡开关元件的氧空位分布，我们在此设计和评估多态忆阻器，作为用于大脑启发式计算的突触连接。充分利用了无成型免遵从操作的优势，我们的设备显示了跨多个电导状态的逐渐切换过渡，满足了ANN中突触连接的切换要求。

更新日期：2020-02-25

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