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Analogue signal and image processing with large memristor crossbars


Memristor crossbars offer reconfigurable non-volatile resistance states and could remove the speed and energy efficiency bottleneck in vector-matrix multiplication, a core computing task in signal and image processing. Using such systems to multiply an analogue-voltage-amplitude-vector by an analogue-conductance-matrix at a reasonably large scale has, however, proved challenging due to difficulties in device engineering and array integration. Here we show that reconfigurable memristor crossbars composed of hafnium oxide memristors on top of metal-oxide-semiconductor transistors are capable of analogue vector-matrix multiplication with array sizes of up to 128 × 64 cells. Our output precision (5–8 bits, depending on the array size) is the result of high device yield (99.8%) and the multilevel, stable states of the memristors, while the linear device current–voltage characteristics and low wire resistance between cells leads to high accuracy. With the large memristor crossbars, we demonstrate signal processing, image compression and convolutional filtering, which are expected to be important applications in the development of the Internet of Things (IoT) and edge computing.

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Fig. 1: Data stored in a 128 × 64 1T1R memristor crossbar, demonstrating conductance state linearity, write precision and accuracy, and read stability and reproducibility.
Fig. 2: Experimental output accuracy and precision for discrete cosine transformation (DCT) using memristor crossbars.
Fig. 3: Experimental realization of a memristor crossbar-based spectrum analyser.
Fig. 4: Experimental 2D DCT demonstration using differential conductance pairs for image compression and processing.
Fig. 5: Experimental convolution demonstration with differential memristor conductance pairs.

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This work was supported in part by the Air Force Research Laboratory (AFRL; grant no. FA8750-15-2-0044), the US Air Force Office for Scientific Research (AFOSR; grant no. FA9550-12-1-0038), the Intelligence Advanced Research Projects Activity (IARPA; contract 2014-14080800008) and the National Science Foundation (NSF; ECCS-1253073). This work was performed in part at the Center for Hierarchical Manufacturing (CHM), an NSF sponsored Nanoscale Science and Engineering Center (NSEC) at University of Massachusetts, Amherst.

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Authors and Affiliations



C.L., H.J., N.G., N.D., P.L. and Z.W. built the integrated chips. C.L., M.H., Y.L. and J.P.S. carried out the measurements. E.M., M.H. and J.P.S. built the measurement system. Y.L., M.H. and W.S. performed circuit simulation. J.Z. took the cross-sectional SEM and TEM images. J.P.S., J.J.Y. and Q.X. designed the experiments and supervised the project. Q.X., C.L., J.J.Y. and R.S.W. wrote the manuscript. All authors contributed to analysis of the results and commented on the manuscript.

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Correspondence to John Paul Strachan, J. Joshua Yang or Qiangfei Xia.

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Supplementary Information

Supplementary Figures 1–16, Supplementary Table 1, and Supplementary Notes 1–4.


Supplementary Video 1

Programming of the conductance of memristors in a 64 × 64 array to arbitrary values within a pre-defined conductance range.

Supplementary Video 2

Real-time crossbar output with changing input frequencies.

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Li, C., Hu, M., Li, Y. et al. Analogue signal and image processing with large memristor crossbars. Nat Electron 1, 52–59 (2018).

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