In this article, an electrostatically-actuated NEMS XOR gate is proposed based on photonic crystals for hardware implementation of binary neural networks. The device includes a 2D photonic crystal which is set on a movable electrode to implement the XOR logic using the transmission of specific wavelengths to the output. This design represents the importance of the proposed structure in which the logic gate operation is not dependent on the contact of its conductive layers. Consequently, one of the major issues in MEMS-based logic gates, which is due to the contact of the operating electrodes and may cause stiction problem, reducing the reliability of the system, can be tackled by the present approach. Furthermore, according to the simulation results, the functional characteristics of the present NEMS XOR gate are obtained as follows: pull-in voltage of V _p = 8V, operating voltage of V _o = 10V and switching time of t _s = 4 μs. The results also show that the proposed design provides a classification error rate of between 1% to 12%, while used in neural network implementation. This error can be negligible compared to the state-of-the-art designs in neural network implementation. These appropriate parameters of the present NEMS gate make it a promising choice for the implementation of neural networks with a high network accuracy even in the presence of significant process variations.

中文翻译：

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基于光学 NEMS 的高速二值神经网络硬件加速器

在本文中，提出了一种基于光子晶体的静电驱动 NEMS 异或门，用于二元神经网络的硬件实现。该器件包括一个设置在可移动电极上的 2D 光子晶体，通过将特定波长传输到输出来实现 XOR 逻辑。该设计代表了所提出的结构的重要性，其中逻辑门操作不依赖于其导电层的接触。因此，基于MEMS的逻辑门的主要问题之一，即由于操作电极的接触而可能导致粘滞问题，从而降低系统的可靠性，可以通过本方法来解决。此外，根据仿真结果，得到本NEMS异或门的功能特性如下：吸合电压V _p = 8V，工作电压V _o = 10V，开关时间t _s = 4 μs。结果还表明，所提出的设计在用于神经网络实现时，分类错误率在 1% 到 12% 之间。与神经网络实现中最先进的设计相比，这个错误可以忽略不计。当前 NEMS 门的这些适当参数使其成为实现神经网络的有前途的选择，即使在存在重大过程变化的情况下也具有高网络精度。