西亚试剂：Training and operation of an integrated neuromorphic networ

发布时间：2025-07-13

Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex1, with its approximately 1014 synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks2 based on circuits3, 4 combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one3 or several4 crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits5, 6, 7, 8, 9, 10, 11, 12, including first demonstrations5, 6, 12 of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks13, 14, 15, 16, 17, 18. Very recently, such experiments have been extended19 to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors11, 20, 21, whose nonlinear current–voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm22 to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.

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