McCulloch-Pitts neuron structures are comprised of a number of synaptic inputs and a decision element, called soma. In this paper, we propose a 5-bit Graphene Nanoribbon (GNR)-based DAC to fulfill the role of the summation element featuring programmable input weights. The proposed GNR-based 5-bit DAC relies on: (i) GNR unit current cells and (ii) a GNR logic thermometric decoding block. Our implementation is based on mapping the GNR structure's conductance using Matlab and performing the required SPICE analysis using the Matlab based GNR Verilog-A model. The unit current cell geometry and bias conditions were chosen based on the unit cell's conductance map from which we derived its ION/IOFF ratio, as well as transfer and output characteristics, resembling the classical MOSFET counterpart. By utilizing GNR devices instead of FinFET counterparts, a reduction of the active area of the 5-bit current DAC by up to a factor of three can be achieved. Furthermore, the GNR implementation achieved this while maintaining comparable INL and DNL performance to that of the FinFET variant, i.e., DNL of [-0.196, 0.088] LSB and INL of [-0.809, 0.364] LSB for the proposed GNR 5-bit DAC while operating at a supply voltage of only 0.2 V.

中文翻译：

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基于石墨烯纳米带的 5 位数模转换器


麦卡洛克-皮茨神经元结构由许多突触输入和一个称为体细胞的决策元素组成。在本文中，我们提出了一种基于 5 位石墨烯纳米带 (GNR) 的 DAC，以履行具有可编程输入权重的求和元件的作用。所提出的基于 GNR 的 5 位 DAC 依赖于：(i) GNR 单位电流单元和 (ii) GNR 逻辑测温解码块。我们的实现基于使用 Matlab 映射 GNR 结构的电导，并使用基于 Matlab 的 GNR Verilog-A 模型执行所需的 SPICE 分析。单位电流单元的几何形状和偏置条件是根据单位单元的电导图来选择的，我们从中得出其 ION/IOFF 比以及传输和输出特性，类似于经典的 MOSFET 对应物。通过使用 GNR 器件而不是 FinFET 器件，可以将 5 位电流 DAC 的有源面积减少多达三倍。此外，GNR 实现实现了这一目标，同时保持了与 FinFET 变体相当的 INL 和 DNL 性能，即建议的 GNR 5 位 DAC 的 DNL 为 [-0.196, 0.088] LSB，INL 为 [-0.809, 0.364] LSB工作电源电压仅为 0.2V。