We study the validity of implementing MoS2 multilevel memories in future neuromorphic networks. Such a validity is determined by the number of available states per memory and their retention characteristics within the nominal computing duration. Our work shows that MoS2 memories have at least 3-bit and 4.7-bit resolvable states suitable for hour-scale and minute-scale computing processes, respectively. The simulated neural network conceptually constructed on the basis of such memory states predicts a high learning accuracy of 90.9% for handwritten digit datasets. This work indicates that multilevel MoS2 transistors could be exploited as valid and reliable nodes for constructing neuromorphic networks.

中文翻译：

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用于神经形态计算的基于 MoS2 的多级晶体管存储器的研究

我们研究了在未来神经形态网络中实现 MoS2 多级记忆的有效性。这种有效性取决于每个存储器的可用状态数量及其在标称计算持续时间内的保留特性。我们的工作表明，MoS2 存储器至少具有 3 位和 4.7 位可解析状态，分别适用于小时级和分钟级计算过程。基于这种记忆状态在概念上构建的模拟神经网络预测手写数字数据集的学习准确率高达 90.9%。这项工作表明，多级 MoS2 晶体管可以用作构建神经形态网络的有效且可靠的节点。