发表机构
International Iberian Nanotechnology Laboratory (INL); Bilkent University; UNAM - Institute of Materials Science and Nanotechnology, Bilkent University; Sabanci University; Koc University（国际伊比利亚纳米技术实验室; 比尔肯特大学; 比尔肯特大学 UNAM 材料科学与纳米技术研究所; 萨班奇大学; 科奇大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过调整NEMS谐振器双模式驱动幅度实现动力学多样性，提升储层计算性能，显著降低NARMA-2误差并扩展计算能力。
AI 中文摘要
物理储层计算利用非线性动力学和训练好的线性读出层来处理信息。纳米机电（NEMS）谐振器将几何Duffing非线性与衰减记忆相结合，但大多数机电实现使用单一谐振模式。在此，我们展示了使用单个NEMS谐振器的两个相互作用模式通过一个读出端口进行储层计算。我们通过互补的模式驱动设置引入动力学多样性：在模式间不同的驱动幅度分配下重放相同的输入序列，并将响应拼接成单一特征矩阵。这种复用扩展了读出层可用的表示，无需额外设备或训练内部参数。在NARMA-2任务上，与单模式操作相比，方差归一化测试误差降低了超过28倍，与最佳的双模式设置相比降低了超过三倍。线性记忆容量测量表明，在测试的符号持续时间下，可访问的回忆仅跨越几个符号。其随延迟的快速下降与机械耗散一致，伴随着NARMA误差的上升，并在更高阶时最终失去复用增益。我们还使用电馈通作为内部参考，以评估NEMS响应的计算贡献。使用相同的记录和匹配的驱动设置及处理，分别训练线性读出层于馈通特征和从测量的NEMS响应中导出的特征。在具有挑战性的非线性映射任务上，复用的NEMS特征产生的误差远低于馈通特征。这些结果展示了通过模式驱动幅度的变化引入的动力学多样性如何扩展单个多模式NEMS谐振器的计算能力。
英文摘要
Physical reservoir computing uses nonlinear dynamics and a trained linear readout to process information. Nanoelectromechanical (NEMS) resonators combine geometric Duffing nonlinearity with fading memory, but most electromechanical implementations use a single resonance mode. Here, we demonstrate reservoir computing with two interacting modes of a single NEMS resonator measured through one readout port. We introduce dynamical diversity through complementary modal drive settings: the same input sequence is replayed under different allocations of drive amplitude between the modes, and the responses are concatenated into a single feature matrix. This multiplexing expands the representation available to the readout without additional devices or training of internal parameters. On NARMA-2, it reduces variance-normalized test error more than 28-fold relative to single-mode operation and more than threefold relative to the best individual two-mode setting. Linear memory-capacity measurements show that accessible recall spans only a few symbols at the tested symbol duration. Its rapid decline with delay, consistent with mechanical dissipation, accompanies rising NARMA error and the eventual loss of multiplexing gains at higher orders. We also use electrical feedthrough as an internal reference for assessing the computational contribution of the NEMS response. Separate linear readouts are trained on feedthrough features and features derived from the measured NEMS response, using the same recordings and matched drive settings and processing. On challenging nonlinear mapping tasks, the multiplexed NEMS features yield substantially lower errors than the feedthrough features. These results demonstrate how dynamical diversity through variations in modal drive amplitudes expands the computational capability of a single multimode NEMS resonator.
Comments51 pages including the Supplementary Information
