Abstract
Using a state-of-the-art quantum transport simulator based on the effective mass approximation, we have thoroughly studied the impact of variability on channel gate-all-around nanowire metal-oxide-semiconductor field-effect transistors (NWFETs) associated with random discrete dopants, line edge roughness, and metal gate granularity. Performance predictions of NWFETs with different cross-sectional shapes such as square, circle, and ellipse are also investigated. For each NWFETs, the effective masses have carefully been extracted from tight-binding band structures. In total, we have generated 7200 transistor samples and performed approximately 10,000 quantum transport simulations. Our statistical analysis reveals that metal gate granularity is dominant among the variability sources considered in this work. Assuming the parameters of the variability sources are the same, we have found that there is no significant difference of variability between SiGe and Si channel NWFETs.
Highlights
Semiconductor fabrication has witnessed amazing progress in the last about 50 to 60 years which has enabled the scaling of the physical dimensions of the metal-oxide-semiconductor field-effect transistors (MOSFETs) at an exponential rate
We focus on the investigation of the impact of dominant sources of statistical variability (RDD, line edge roughness (LER) and metal gate granularity (MGG)) in n-type SixGe1−x channel GAA nanowire MOSFETs (NWFETs) with different cross-section shapes
It is found that the median of subthreshold slope (SS) with random discrete dopants (RDD), LER, and MGG is 62.8 mV/dec, which is comparable to the value of SS (63.0 mV/dec) for the corresponding ideal device
Summary
Semiconductor fabrication has witnessed amazing progress in the last about 50 to 60 years which has enabled the scaling of the physical dimensions of the metal-oxide-semiconductor field-effect transistors (MOSFETs) at an exponential rate. Previous simulation studies have shown that random discrete dopants (RDD), line edge roughness (LER), and metal gate granularity (MGG) induce significant variability in ultra-scaled InGaAs [16] and Si [17,18] channel nanoscale devices The former used classical transport models, whereas the latter considered a very small number of statistical samples due to the computational cost of quantum transport simulations.
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