A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations


Memristors and memristor crossbar arrays have been widely studied for neuromorphic and other in-memory computing applications. To achieve optimal system performance, however, it is essential to integrate memristor crossbars with peripheral and control circuitry. Here, we report a fully functional, hybrid memristor chip in which a passive crossbar array is directly integrated with custom-designed circuits, including a full set of mixed-signal interface blocks and a digital processor for reprogrammable computing. The memristor crossbar array enables online learning and forward and backward vector-matrix operations, while the integrated interface and control circuitry allow mapping of different algorithms on chip. The system supports charge-domain operation to overcome the nonlinear IV characteristics of memristor devices through pulse width modulation and custom analogue-to-digital converters. The integrated chip offers all the functions required for operational neuromorphic computing hardware. Accordingly, we demonstrate a perceptron network, sparse coding algorithm and principal component analysis with an integrated classification layer using the system.

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Fig. 1: Fully integrated memristor/CMOS chip.
Fig. 2: Experimental demonstration of the single-layer perceptron on the integrated memristor chip.
Fig. 3: Experimental demonstration of sparse coding using the integrated memristor chip.
Fig. 4: Experimental demonstration of PCA using the integrated chip.
Fig. 5: Classification results in the bilayer network.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.


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The authors acknowledge inspiring discussions with C. Liu, T. Chou, P. Brown, M.A. Zidan and P.M. Sheridan. This work was supported in part by the Defense Advanced Research Projects Agency (DARPA) through award HR0011-13-2-0015, the National Science Foundation (NSF) through awards CCF-1617315 and 1734871, and the Applications Driving Architectures (ADA) Research Centre, a JUMP Centre co-sponsored by SRC and DARPA.

Author information




F.C., J.M.C., S.H.L., Z.Z., M.P.F. and W.D.L. conceived the project and constructed the research frame. S.H.L. prepared the memristor arrays and performed device integration. J.M.C., Y.L., V.B., Z.Z. and M.P.F. designed the CMOS chip. F.C. and J.M.C. prepared the test hardware and software platform. F.C. and S.H.L. performed the network measurements and software simulations. W.D.L. directed the project. F.C., J.M.C., S.H.L., Z.Z., M.P.F. and W.D.L. analysed the experimental data and wrote the manuscript. All authors discussed the results and implications and commented on the manuscript at all stages.

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Correspondence to Wei D. Lu.

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Cai, F., Correll, J.M., Lee, S.H. et al. A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations. Nat Electron 2, 290–299 (2019). https://doi.org/10.1038/s41928-019-0270-x

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