Joint impact of random variations and RTN on dynamic writeability in 28nm bulk and FDSOI SRAM

Zimmer, Brian; Thomas, Olivier; Toh, Seng Oon; Vincent, Taylor; Asanović, Krste; Nikolić, Borivoje

doi:10.1109/essderc.2014.6948767

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…For the RTN amplitude per trap, two popular statistical distributions used in early works are Lognormal [1], [5]- [8], and Exponential [3]- [6], [28], [29]. In addition, Gumbel distribution has been used to capture the long tail of RTN [9]- [11].…”

Section: Results and Discussion A Problems With The Proposed Smentioning

confidence: 99%

“…It has received many attentions, as it adversely affects the operation of electronic circuits [1]- [15]. As MOSFETs become smaller, RTN becomes increasingly important, driven by an increased impact of a single charge on smaller devices and an increase in the number of devices in a system [1]- [8]. A large number of devices in a system will contain more devices in the tail of statistical distributions, which can cause errors.…”

Section: Introductionmentioning

confidence: 99%

“…It is rare to have a trap located just above this percolation path and such a trap will cause a large δVth and result in the long distribution tail [2], [6], [7], [24]. Modelling the long tail in the CDF is a tall order and three statistical distributions have been proposed: Exponential [3]- [6], [23], [25]- [29], Lognormal [1], [5]- [8], [30]- [32] and Gumbel [9]- [11]. The success of RTN modelling in term of yield prediction for a system, such as SRAM, requires an accurate statistical distribution tail [2], [5], [7], [30], [31].…”

Section: Introductionmentioning

confidence: 99%

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On the Accuracy in Modeling the Statistical Distribution of Random Telegraph Noise Amplitude

Mehedi

Tok

et al. 2021

IEEE Access

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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.

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Section: Results and Discussion A Problems With The Proposed Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Accuracy in Modeling the Statistical Distribution of Random Telegraph Noise Amplitude

Mehedi

Tok

et al. 2021

IEEE Access

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“…In an 8-T SRAM, the write operation works in the same way as in a 6-T SRAM, but the read operation works quite differently [7][8][9]. To write data in an 8-T SRAM cell, the write word-line voltage (V WL ) should be increased to V DD so that the corresponding cell can be selected/enabled, and then PG3 and PG4 in the cell become 'on'.…”

Section: Ncfet-based 8-t Sram Bit-cellmentioning

confidence: 99%

“…For a 6-T SRAM read operation, BL and ∼BL are all pre-charged and V WL is increased to V DD . Then, the outputs of inverters A and B approach ∼BL and BL, respectively [8]. However, the read operation of the 8-T SRAM cell is initiated by holding the voltage of both the RBL and RWL at 'high', and the WWL is set to '0'.…”

Section: Ncfet-based 8-t Sram Bit-cellmentioning

confidence: 99%

Performance analysis and yield estimation for a negative capacitance field effect transistor-based eight-transistor static random access memory

Hong¹,

Han²,

Shin³

2021

Semicond. Sci. Technol.

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The read/write performance and yield of a negative capacitance field effect transistor (NCFET)-based eight-transistor (8-T) static random access memory (SRAM) are quantitatively evaluated and then compared with a conventional 8-T SRAM. The performance of the 8-T SRAM cell is analyzed by read/write metrics (i.e. read static noise margin, write-ability current and read 'zero (0)' current). The sensitivity of the 8-T SRAM cell to the read/write metric is estimated by quantitatively evaluating the impact of systematic variation (i.e. channel width, length and threshold voltage variation). Based on variation-aware sensitivity analysis, quantitative yield estimation is done using the 'cell sigma' concept. Finally, the minimum power supply voltage (V DD ) (which satisfies six cell sigma for yield estimation of a NCFET-based/baseline 8-T SRAM cell) is quantitatively estimated.

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Advanced Characterization and Analysis of Random Telegraph Noise in CMOS Devices

Martín-Martínez

Rodrı́guez

Nafrı́a

2020

Noise in Nanoscale Semiconductor Devices

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Joint impact of random variations and RTN on dynamic writeability in 28nm bulk and FDSOI SRAM

Cited by 6 publications

References 5 publications

On the Accuracy in Modeling the Statistical Distribution of Random Telegraph Noise Amplitude

On the Accuracy in Modeling the Statistical Distribution of Random Telegraph Noise Amplitude

Performance analysis and yield estimation for a negative capacitance field effect transistor-based eight-transistor static random access memory

Advanced Characterization and Analysis of Random Telegraph Noise in CMOS Devices

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