In this paper we propose and evaluate the performance of a 3D-embedded neuromorphic computation block based on indium gallium zinc oxide ( -IGZO) based nanosheet transistor and bi-layer resistive memory devices. We have fabricated bi-layer resistive random-access memory (RRAM) devices with Ta2O5 and Al2O3 layers. The device has been characterized and modeled. The compact models of RRAM and -IGZO based embedded nanosheet structures have been used to evaluate the system level performance of 8 vertically stacked -IGZO based nanosheet layers with RRAM for neuromorphic applications. The model considers the design space with uniform bit line (BL), select line (SL) and word line (WL) resistance. Finally, we have simulated the weighted sum operation with our proposed 8-layer stacked nanosheet based embedded memory and evaluated the performance for VGG-16 convolutional neural network (CNN) for Fashion-MNIST and CIFAR-10 data recognition, which yielded 92% and 75% accuracy respectively with drop out layers amid device variation.

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Dense analog synaptic crossbar arrays are a promising candidate for neuromorphic hardware accelerators due to the ability to mitigate data movement by performing in-situ vector-matrix products and weight updates within the storage array itself. However, many analog weight storage cells suffer from long latencies or low dynamic ranges, limiting the achievable performance. In this work, we demonstrate that the voltage-controlled partial polarization switching dynamics in ferroelectric-field-effect transistors (FeFET) can be harnessed to enable a 32 state non-volatile analog synaptic weight cell with large dynamic range (67×) and low latency weight updates (50 ns) for an amplitude modulated pulse scheme.

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