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Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.

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We present a thorough investigation of the random telegraph noise scaling trend for both NAND and NOR floating-gate Flash memories, including experimental and physics-based modeling results. The statistical distribution of the random telegraph noise amplitude is computed using conventional 3D TCAD simulations, establishing a direct connection with cell parameters. The analysis results in a simple formula for the random telegraph noise amplitude standard deviation as a function of cell width, length, substrate doping, tunnel oxide thickness and drain bias. All the simulation results are in good agreement with experimental data and are of utmost importance to understand the random telegraph noise instability and to control it in the development of next generation Flash technologies.

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