With the onset of the era of big data, memristors have been extensively studied for applications in nonvolatile neuromorphic computing due to their fast switching speed and low switching power ability and the complementary metal–oxide–semiconductor (CMOS) compatibility. In particular, their adjustable cutoff frequency for filtering applications is a significant advantage. However, for circuit applications, memristors are still in their early simulated stage. Herein, Ag/HfO₂/graphene oxide quantum dot (GOQD)/Pt structure memristor devices are fabricated, where GOQDs are used as a stability boost, with biosynapse simulation achieved and numerical recognition performed at an accuracy of 90.91%. A circuit‐based filter is designed based on the memristor and STM32 microcontroller chip controlling, in which low‐pass, high‐pass, and band‐pass filter circuits are all realized. By changing the output signal of the control circuit of the resistance value of the memristor, the cutoff frequency of the filter is successfully adjusted. This work, therefore, paves a new way to obtain filter circuit applications of memristors for electronic systems; the overall performance of the GOQD‐inserted memristor is further optimized and its function in filtering realized, creating opportunities for further application of the memristor in information processing.

中文翻译：

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高性能忆阻器及其滤波电路应用

随着大数据时代的到来，忆阻器因其快速的开关速度和低的开关功率能力以及互补的金属氧化物半导体（CMOS）兼容性而在非易失性神经形态计算中进行了广泛的研究。特别是，它们可调节的截止频率用于滤波应用是一个重要的优势。但是，对于电路应用，忆阻器仍处于早期仿真阶段。在此，Ag / HfO ₂制作了氧化石墨烯量子点（GOQD）/ Pt结构忆阻器器件，其中将GOQD用于提高稳定性，并实现了生物突触模拟并以90.91％的精度进行了数值识别。基于忆阻器和STM32微控制器芯片控制设计了基于电路的滤波器，其中均实现了低通，高通和带通滤波器电路。通过改变忆阻器的电阻值的控制电路的输出信号，可以成功地调整滤波器的截止频率。因此，这项工作为获得电子系统忆阻器的滤波电路应用开辟了一条新途径。进一步优化了插入GOQD的忆阻器的整体性能，并实现了其过滤功能，