We take an adaptive leaky integrate-and-fire neuron model to explore the effect of non-Poisson neurotransmitter on stochastic resonance and its signal-to-noise ratio(SNR)gain.Event triggered algorithm is adopted to sp...We take an adaptive leaky integrate-and-fire neuron model to explore the effect of non-Poisson neurotransmitter on stochastic resonance and its signal-to-noise ratio(SNR)gain.Event triggered algorithm is adopted to speed up the simulating process.It is revealed that both the output SNR and the SNR gain can be monotonically improved when increasing the shape parameter for Gamma distribution.Particularly,for large signal coupling strength,the 1:1 stochastic phase locking induced by Gamma noise is responsible for the frequency matching stochastic resonance,and the output signal-to-noise ratio can surpass the input signal-to-noise ratio,which is significantly different with Poisson case,while for extremely weak signal coupling strength,the SNR gain peak,which is far larger than unity,is due to noise induced resonance.The observations are meaningful in understanding the neural processing mechanisms from a more realistic viewpoint of synaptic modeling.展开更多
We propose an accurate model to describe the I-V characteristics of a sub-90-nm metal-oxide-semiconductor field-effect transistor(MOSFET) in the linear and saturation regions for fast analytical calculation of the cur...We propose an accurate model to describe the I-V characteristics of a sub-90-nm metal-oxide-semiconductor field-effect transistor(MOSFET) in the linear and saturation regions for fast analytical calculation of the current.The model is based on the BSIM3v3 model.Instead of using constant threshold voltage and early voltage,as is assumed in the BSIM3v3 model,we define these voltages as functions of the gate-source voltage.The accuracy of the model is verified by comparison with HSPICE for the 90-,65-,45-,and 32-nm CMOS technologies.The model shows better accuracy than the nth-power and BSIM3v3 models.Then,we use the proposed I-V model to calculate the read static noise margin(SNM) of nano-scale conventional 6T static random-access memory(SRAM) cells with high accuracy.We calculate the read SNM by approximating the inverter transfer voltage characteristic of the cell in the regions where vertices of the maximum square of the butterfly curves are placed.The results for the SNM are also in excellent agreement with those of the HSPICE simulation for 90-,65-,45-,and 32-nm technologies.Verification in the presence of process variations and negative bias temperature instability(NBTI) shows that the model can accurately predict the minimum supply voltage required for a target yield.展开更多
基金the Non Poisson Modeling of Neuron Synaptic Input and Critical Dynamics for Cortical Networks(Grant No.11772241).
文摘We take an adaptive leaky integrate-and-fire neuron model to explore the effect of non-Poisson neurotransmitter on stochastic resonance and its signal-to-noise ratio(SNR)gain.Event triggered algorithm is adopted to speed up the simulating process.It is revealed that both the output SNR and the SNR gain can be monotonically improved when increasing the shape parameter for Gamma distribution.Particularly,for large signal coupling strength,the 1:1 stochastic phase locking induced by Gamma noise is responsible for the frequency matching stochastic resonance,and the output signal-to-noise ratio can surpass the input signal-to-noise ratio,which is significantly different with Poisson case,while for extremely weak signal coupling strength,the SNR gain peak,which is far larger than unity,is due to noise induced resonance.The observations are meaningful in understanding the neural processing mechanisms from a more realistic viewpoint of synaptic modeling.
文摘We propose an accurate model to describe the I-V characteristics of a sub-90-nm metal-oxide-semiconductor field-effect transistor(MOSFET) in the linear and saturation regions for fast analytical calculation of the current.The model is based on the BSIM3v3 model.Instead of using constant threshold voltage and early voltage,as is assumed in the BSIM3v3 model,we define these voltages as functions of the gate-source voltage.The accuracy of the model is verified by comparison with HSPICE for the 90-,65-,45-,and 32-nm CMOS technologies.The model shows better accuracy than the nth-power and BSIM3v3 models.Then,we use the proposed I-V model to calculate the read static noise margin(SNM) of nano-scale conventional 6T static random-access memory(SRAM) cells with high accuracy.We calculate the read SNM by approximating the inverter transfer voltage characteristic of the cell in the regions where vertices of the maximum square of the butterfly curves are placed.The results for the SNM are also in excellent agreement with those of the HSPICE simulation for 90-,65-,45-,and 32-nm technologies.Verification in the presence of process variations and negative bias temperature instability(NBTI) shows that the model can accurately predict the minimum supply voltage required for a target yield.
