The spectrum of two-dimensional (2D) and layered materials 'beyond graphene' offers a remarkable platform to study new phenomena in condensed matter physics. Among these materials, layered hexagonal boron nitride (hBN), with its wide bandgap energy (∼5.0-6.0 eV), has clearly established that 2D nitrides are key to advancing 2D devices. A gap, however, remains between the theoretical prediction of 2D nitrides 'beyond hBN' and experimental realization of such structures. Here we demonstrate the synthesis of 2D gallium nitride (GaN) via a migration-enhanced encapsulated growth (MEEG) technique utilizing epitaxial graphene. We theoretically predict and experimentally validate that the atomic structure of 2D GaN grown via MEEG is notably different from reported theory. Moreover, we establish that graphene plays a critical role in stabilizing the direct-bandgap (nearly 5.0 eV), 2D buckled structure. Our results provide a foundation for discovery and stabilization of 2D nitrides that are difficult to prepare via traditional synthesis.

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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