On the Accuracy in Modeling the Statistical Distribution of Random Telegraph Noise Amplitude
| Autor: | John Marsland, Zhigang Ji, Kean Hong Tok, Jian Fu Zhang, Weidong Zhang, Zengliang Ye, Mehzabeen Mehedi |
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| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
TK Monte Carlo method 02 engineering and technology 01 natural sciences Noise (electronics) Gumbel distribution 0103 physical sciences 0202 electrical engineering electronic engineering information engineering General Materials Science Statistical physics Mathematics 010302 applied physics time dependent variations General Engineering Statistical model yield 020202 computer hardware & architecture Exponential function Random telegraph noise (RTN) jitters TA Log-normal distribution Generalized extreme value distribution Probability distribution lcsh:Electrical engineering. Electronics. Nuclear engineering traps lcsh:TK1-9971 device variations |
| Zdroj: | IEEE Access, Vol 9, Pp 43551-43561 (2021) |
| ISSN: | 2169-3536 |
| Popis: | The power consumption of digital circuits is proportional to the square of operation voltage and the demand for low power circuits reduces the operation voltage towards the threshold of MOSFETs. A weak voltage signal makes circuits vulnerable to noise and the optimization of circuit design requires modelling noise. Random Telegraph Noise (RTN) is the dominant noise for modern CMOS technologies and Monte Carlo modelling has been used to assess its impact on circuits. This requires statistical distributions of RTN amplitude and three different distributions were proposed by early works: Lognormal, Exponential, and Gumbel distributions. They give substantially different RTN predictions and agreement has not been reached on which distribution should be used, calling the modelling accuracy into questions. The objective of this work is to assess the accuracy of these three distributions and to explore other distributions for better accuracy. A novel criterion has been proposed for selecting distributions, which requires a monotonic reduction of modelling errors with increasing number of traps. The three existing distributions do not meet this criterion and thirteen other distributions are explored. It is found that the Generalized Extreme Value (GEV) distribution has the lowest error and meet the new criterion. Moreover, to reduce modelling errors, early works used bimodal Lognormal and Exponential distributions, which have more fitting parameters. Their errors, however, are still higher than those of the monomodal GEV distribution. GEV has a long distribution tail and predicts substantially worse RTN impact. The work highlights the uncertainty in predicting the RTN distribution tail by different statistical models. |
| Databáze: | OpenAIRE |
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