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In this paper we propose and evaluate the performance of a 3D-embeddedneuromorphic computation block based on indium gallium zinc oxide($\alpha$-IGZO) based nanosheet transistor and bi-layer resistive memorydevices. We have fabricated bi-layer resistive random-access memory (RRAM)devices with Ta$_2$O$_5$ and Al$_2$O$_3$ layers. The device has beencharacterized and modeled. The compact models of RRAM and $\alpha$-IGZO basedembedded nanosheet structures have been used to evaluate the system-levelperformance of 8 vertically stacked $\alpha$-IGZO based nanosheet layers withRRAM for neuromorphic applications. The model considers the design space withuniform bit line (BL), select line (SL), and word line (WL) resistance.Finally, we have simulated the weighted sum operation with our proposed 8-layerstacked nanosheet-based embedded memory and evaluated the performance forVGG-16 convolutional neural network (CNN) for Fashion-MNIST and CIFAR-10 datarecognition, which yielded 92% and 75% accuracy respectively with drop outlayers amid device variation.

Ultra Low Power 3D-Embedded Convolutional Neural Network Cube Based on $\alpha$-IGZO Nanosheet and Bi-Layer Resistive Memory