Variable Cutoff Frequency Low-Pass Attenuator Based on Memristor with Sharp Roll-Off Characteristic
Frequency-selective attenuation is widely needed in integrated analog front-ends, yet conventional on-chip RC low-pass filters occupy unfeasibly large silicon areas for low-frequency cutoffs and inherently introduce cumulative phase lag. Motivated by the nonlinear, frequency-dependent state evolution of memristive devices, this work experimentally demonstrates a highly compact, capacitor-free memristor–resistor network that functions as a variable-cutoff, zero-phase-lag resistive attenuator. An Au/HfO2/Au memristor (15 µm × 15 µm) is connected in series with a load resistor and characterized over a wide frequency range. By leveraging the finite time constant of internal ionic drift, the attenuation bandwidth is strictly programmable via the device’s initial resistance. Cutoff frequencies of approximately 10 Hz, 1 kHz, and 10 kHz are achieved for initial resistances of 400 kΩ±30 kΩ, 300 kΩ±30 kΩ, and 200 kΩ±30 kΩ, respectively. Remarkably, the nonlinear state-switching mechanism enables a steep post-cutoff attenuation rate approaching −60 dB/dec—equivalent to a cascaded third-order RC network—using only a single nanoscale device. Rather than functioning as a strictly linear time-invariant (LTI) filter, the proposed circuit operates as a state-adaptive edge-processor. Its inherent amplitude-dependent dynamics and total absence of reactive poles make it exceptionally suited for highly specialized, area-constrained applications, including zero-phase closed-loop noise suppression, frequency-to-amplitude conversion, and amplitude-aware event-driven sensory preprocessing.
Authors
- Jie Lian
- Xingyu Liao
- Li Wang (ORCID: https://orcid.org/0000-0002-0496-9500)
- Yan Wang
- Yang Liu
- Shuang Liu
Institutions
- University of Electronic Science and Technology of China (CN)
- National University of Defense Technology (CN)
- National Engineering Research Center of Electromagnetic Radiation Control Materials (CN)
- State Key Laboratory of Remote Sensing Science (CN)
Publication Details
- Journal
- Electronics
- Published
- 2026-03-11
- DOI
- https://doi.org/10.3390/electronics15061164
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Education Department of Sichuan Province