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Full-Text Articles in Nanotechnology Fabrication
Virtual-Source Based Accurate Model For Predicting Noise Behavior At High Frequencies In Nanoscale Pmos Soi Transistors, Vaibhav R. Ramachandran, Saeed Mohammadi, Sutton Hathorn
Virtual-Source Based Accurate Model For Predicting Noise Behavior At High Frequencies In Nanoscale Pmos Soi Transistors, Vaibhav R. Ramachandran, Saeed Mohammadi, Sutton Hathorn
The Summer Undergraduate Research Fellowship (SURF) Symposium
Complementary Metal Oxide Semiconductor (CMOS) technology at the nanometre scale is an excellent platform to implement monolithically integratedsystems because of the low cost of manufacturing and ease of integration. Newly developed CMOS Silicon on Insulator (SOI) transistors that are currentlydeveloped are suitable for use in radio frequency circuits. They find applications in many areas such as 5G telecommunication systems, high speed Wi-Fi andairport body-scanners. Unfortunately, the models for CMOS SOI transistors that are currently used in these circuits are inaccurate because of their complexity.The models currently used require the optimization of more than 200 variables. This paper aims to accurately …
Reward Modulated Spike Timing Dependent Plasticity Based Learning Mechanism In Spiking Neural Networks, Shrihari Sridharan, Gopalakrishnan Srinivasan, Kaushik Roy
Reward Modulated Spike Timing Dependent Plasticity Based Learning Mechanism In Spiking Neural Networks, Shrihari Sridharan, Gopalakrishnan Srinivasan, Kaushik Roy
The Summer Undergraduate Research Fellowship (SURF) Symposium
Spiking Neural Networks (SNNs) are one of the recent advances in machine learning that aim to further emulate the computations performed in the human brain. The efficiency of such networks stems from the fact that information is encoded as spikes, which is a paradigm shift from the computing model of the traditional neural networks. Spike Timing Dependent Plasticity (STDP), wherein the synaptic weights interconnecting the neurons are modulated based on a pair of pre- and post-synaptic spikes is widely used to achieve synaptic learning. The learning mechanism is extremely sensitive to the parameters governing the neuron dynamics, the extent of …