The advanced progress of electronic-based devices for artificial neural networks and recent trends in neuromorphic engineering are discussed in this review. Recent studies indicate that the memristor and transistor are two types of devices that can be implemented as neuromorphic devices. The electrical switching characteristics and physical mechanism of neuromorphic devices based on metal oxide, metal sulfide, silicon, and carbon materials are broadly covered in this review. Moreover, the switching performance comparison of several materials mentioned above are well highlighted, which would be useful for the further development of memristive devices. Recent progress in synaptic devices and the application of a switching device in the learning process is also discussed in this paper.
In this paper, an enhanced compact model of multiwalled carbon nanotube (MWCNT) interconnects while considering defects and contact resistance is proposed. Based on the atomistic-level simulations, we have found that defect densities impact MWCNT resistance and ultimately their electrical performance. Furthermore, we have computed by atomisticlevel simulations the end-contact resistance between single-wall carbon nanotube (SWCNT) and Palladium (Pd) electrode to mimic the Pd-CNT end-contact resistance of each CNT shell in MWCNT. We have developed an advanced shell-by-shell model to include various parameters such as shell diameter, shell chirality, defects on each shell, and connectivity of each shell to endcontacts. We run Monte Carlo simulations to perform variability studies on each of these parameters to understand the electrical performance variation on MWCNT interconnects. We present simulation results to convey the critical impact of variations. The impact of doping on MWCNT variability in the form of Fermi level shift will be addressed in Part II of this paper.
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