Fig. 1
From: Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Memristive platform for in situ learning. a An optical image of a wafer with transistor arrays. b Close-up of chip image showing arrays of various sizes. c Microscope image showing the 1T1R (one transistor one memristor) structure of the cell. Scale bar, 10 µm. d Cross-sectional scanning electron microscopic image of an individual 1T1R cell, which is cut in a focused ion beam microscope from the dashed line in c. Scale bar, 2 µm. e Cross-sectional transmission electron microscopic image of the integrated Ta/HfO2/Pt memristor. Scale bar, 2 nm. f All responsive devices over 20 potentiation/depression epochs of 200 pulses each. g Evolution of conductance during 20 cycles of full potentiation and depression for a single cell with 200 pulses per cycle, showing low cycle-to-cycle variability. More results are shown in Supplementary Fig. 1. h Evolution of conductance over one 200-pulse cycle of full potentiation and depression for all responsive devices in the array, with median conductance indicated by the yellow line. i Conductance of a 128 × 64 array after single-pulse conductance writing of the discrete Fourier transform matrix. Several stuck devices are visible (in yellow)
