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Discrete distribution of implanted and annealed arsenic atoms in silicon nanowires and its effect on device performance
Nanoscale Research Letters volume 7, Article number: 685 (2012)
Abstract
We have theoretically investigated the effects of random discrete distribution of implanted and annealed arsenic (As) atoms on device characteristics of silicon nanowire (Si NW) transistors. Kinetic Monte Carlo simulation is used for generating realistic random distribution of active As atoms in Si NWs. The active As distributions obtained through the kinetic Monte Carlo simulation are introduced into the source and drain extensions of ntype gateallaround NW transistors. The current–voltage characteristics are calculated using the nonequilibrium Green's function method. The calculated results show significant fluctuation of the drain current. We examine the correlation between the drain current fluctuation and the factors related to random As distributions. We found that the fluctuation of the number of dopants in the source and drain extensions has little effect on the oncurrent fluctuation. We also found that the oncurrent fluctuation mainly originated from the randomness of interatomic distances of As atoms and hence is inherent in ultrasmall NW transistors.
Background
Fluctuation due to random discrete dopant (RDD) distribution is becoming a major concern for continuously scaled down metaloxide semiconductor fieldeffect transistors (MOSFETs) [1–4]. For ultrasmall MOSFETs, not only random location of individual dopant atoms but also fluctuation of the number of active impurities is expected to have significant impacts on the device performance. Effects of the RDD distribution are usually analyzed with a randomly generated RDD distribution. The actual RDD distribution, however, should be correlated with the process condition and can be different from a mathematically generated one. In the present study, we investigate the effects of random discrete distribution of implanted and annealed arsenic (As) atoms in source and drain (S/D) extensions on the characteristics of ntype gateallaround (GAA) silicon nanowire (Si NW) transistors. We investigate a GAA Si NW transistor since it is considered as a promising structure for ultimately scaled CMOS because of its excellent gate control [2, 5–7]. Kinetic Monte Carlo (KMC) simulation is used for generating realistic random distribution of active As atoms in Si NWs. The current–voltage characteristics are then calculated using the nonequilibrium Green's function (NEGF) method. Our results demonstrate that the oncurrent fluctuation mainly originated from the randomness of the dopant location and hence is inherent in ultrasmall NW transistors.
Methods
Random discrete As distribution in a Si NW is calculated using Sentaurus KMC simulator (Synopsys, Inc., Mountain View, CA, USA) [8–10]. Figure 1 shows an example of the calculated discrete As distribution in a Si NW (3 nm wide, 3 nm high, and 10 nm long) with 1nmthick oxide. The Si NW is implanted with As (0.5 keV, 1 × 10^{15} cm^{−2}) and annealed at 1,000°C with a hold time of 0 s. Statistical variations are investigated using 200 different random seeds. The active As distributions obtained through the KMC simulation are then introduced into the S/D extensions of ntype Si NW MOSFETs, whose device structure is given in Figure 2. In the present study, we consider only an intrinsic channel, and impacts of possible penetration of dopant atoms into the channel region are not examined. To mimic metal electrodes, the S/D regions are heavily doped with N_{d} = 5 × 10^{20} cm^{−3} (continuously doping). We simulate 100 samples using 200 different random seeds (each sample needs two random seeds for S/D extensions). The drain currentgate voltage (I_{d}V_{g}) characteristics are calculated using the NEGF method with an effective mass approximation [11, 12]. The discrete impurities are treated with a cloudincell charge assignment scheme [13]. Phonon scattering is not taken into account in the present calculation.
Results and discussion
As distribution by KMC simulation
Figure 3 shows random discrete active As distribution in the Si NW calculated by the KMC simulation. The histogram shows the normal distribution curve, and therefore, 200 seeds are large enough to represent the randomness. The average number of active As atoms in the NW is 27 with the standard deviation of 5. Out of 300 As atoms implanted into the 3nmwide Si region, only approximately 10% of As atoms are active in the Si NW. Most of the As atoms are in the oxide (approximately 40 atoms), at the oxide/Si interface (approximately 50), in Asvacancy (AsV) clusters (approximately 90), and As precipitates (approximately 90) (see Figure 1). AsV clusters and As precipitates are inactive and immobile. They are formed when As concentration exceeds approximately 10^{20} cm^{−3} (for AsV clusters) and the solubility limit (for As precipitates) [14, 15]. In Sentaurus, not only AsV clusters but also AsSi interstitial (I) clusters (inactive and immobile) are taken into account, but As precipitates are not. In the present study, therefore, AsSi interstitial clusters in Sentaurus are interpreted as As precipitates. The calculation results show that the As activation ratio decreases with higher As dose because inactive As species (AsV clusters and As precipitates) are more likely to be formed. In NWs with smaller widths and heights, the As activation is found to be lower because more As atoms are closer to the oxide/Si interface and hence are piled up at the interface.
NEGF simulation
Figure 4 shows the I_{d}V_{g} characteristics at V_{d} = 0.5 V of 100 devices with different discrete As distributions (gray lines). In the figure, their average value 〈I_{d}〉 (open circles) and the I_{d} of a continuously doping case in the S/D extensions (solid circles) are also shown for comparison. For the continuously doping case, the S/D extensions are uniformly ndoped with a concentration of 3 × 10^{20} cm^{−3}, which corresponds to the average active As concentration in the Si NWs (see Figure 3). The IV characteristics of devices uniformly ndoped with 2 × 10^{20}, 2.5× 10^{20}, and 3.5 × 10^{20} cm^{−3} are also calculated, and the results show only slight differences (within 10%) compared with the 3 × 10^{20} cm^{−3} case. Figure 5 represents the carrier density profiles and the location of active As atoms in some representative devices. Equidensity surfaces at V_{d} = V_{g} = 0.5 V (blue and green surfaces for 3 × 10^{20} and 1 × 10^{20} cm^{−3}, respectively) and dopant positions (yellow dots) are shown. Figure 5 (a), (b), (c), and (d) correspond to the IV characteristics of continuously doped (solid circles in Figure 4), highcurrent (red dashed line), mediumcurrent (green dashed line), and lowcurrent (blue dashed line) devices, respectively. The drain current of NW devices with random discrete As distribution is found to be reduced compared to that with uniform As distribution. This reduction is ascribed to ionized impurity scattering, which is taken into account for random As distribution, but not for uniform As distribution. The normalized average current 〈I_{d}〉/I_{0} (I_{0} is the drain current of the continuously doped device) is found to be approximately 0.8 and decreases with V_{g}, as shown in Figure 6. The standard deviation of the 100 samples is found to be σI_{d} ~ 0.2〈I_{d}〉.
Drain current fluctuation
In order to investigate the cause of the drain current fluctuation, we examine the correlation between I_{d} and the factors related to random As distributions. The factors are extracted from the random As positions, based on a simple onedimensional model as schematically shown in Figure 7, where blue dots represent active As atoms. The factors are an effective gate length (L_{g}^{*}), standard deviations of interatomic distances in the S/D extensions (σ_{s} and σ_{d}), their sum (σ = σ_{s} + σ_{d}), and the maximum separation between neighboring impurities in the S extension (S_{s}), in the D extension (S_{d}), and in the S/D extensions (S). The effects of the number of As dopants in the S/D extensions are also examined, with the factors of the number of active As in the S extension (N_{s}), in the D extension (N_{d}), and in the S/D extensions (N). Figure 8 represents the correlation between I_{d} and these factors, and Table 1 summarizes the correlation coefficients for the offstate (V_{g} = 0 V) and the onstate (V_{g} = 0.5 V) at V_{d} = 0.05 and 0.5 V. The correlation coefficient r is classified as follows: 0.0 < r < 0.2, little correlation; 0.2 < r < 0.4, weak correlation; 0.4 < r < 0.7, significant correlation; 0.7 < r < 0.9, strong correlation; and 0.9 < r < 1.0, extremely strong correlation. We highlight clear correlations in Table 1. Note that the threshold voltage is closely related to the offcurrent because I_{d} varies exponentially with V_{g} at the subthreshold region.
Significant correlations between I_{d} and L_{g}^{*} are found at the offstate with V_{d} of both 0.05 and 0.5 V. Negative correlation means that I_{d} tends to decrease with increasing L_{g}^{*}. The sum of the standard deviations of interatomic distances in the S/D extensions (σ) shows a clear correlation at the onstate with V_{d} = 0.05 V. Concerning the maximum separation, a clear correlation at the onstate with V_{d} = 0.5 V and that with V_{d} = 0.05 V are found with S_{s} and S, respectively, while little correlation with S_{d} is seen at any cases. These results demonstrate that the effective gate length (L_{g}^{*}) is a main factor for the offstate, where the channel potential mainly governs the I V characteristics. We mention that the offcurrent becomes larger when active As atoms penetrate into the channel region, which is not taken into account in the present simulation. This increase in offcurrent can be explained in terms of the ioninduced barrier lowering [16], where the potential barrier in the channel is significantly lowered by attractive donor ions, which enhances the electron injection from the source. For the onstate, random As distribution in the S extension (S_{s}) is an important factor at high V_{d} due to current injection from S, and that in the S/D extensions (σ and S) is dominant at low V_{d} because the backflow current from D also contributes the current.
On the other hand, little or weak correlations between I_{d} and the number of As dopants are found. The weak positive correlations with N_{s} and N at the offstate are attributed to a tendency that a larger number of dopants lead to smaller L_{g}^{*}. In order to further investigate the effect of the number of As, I_{d}V_{g} characteristics of NWs implanted at a smaller dose of 2 × 10^{14} cm^{−2} were calculated. The average number of active As atoms in this NW is 16, which averages 1.8 × 10^{20} cm^{−3}. The average and standard deviation of the oncurrent in this NW are almost the same as those in the 1 × 10^{15} cm^{−2} NW. This is consistent with little or weak correlations between I_{d} and the number of As dopants as we mentioned above. However, a few out of 100 NW devices of 2 × 10^{14} cm^{−2} have oncurrent which is only about one half its average. This is attributable to the large interatomic distances of discrete As atoms in these devices. These results indicate that the oncurrent fluctuation is caused by the fluctuation of interatomic distances of discrete As atoms, not by the fluctuation of the number of As. The offcurrent fluctuation can be reduced by a process in which dopants in the S/D extensions are likely to exist near the channel region. In contrast, the oncurrent fluctuation may be inherent in ultrasmall NW transistors because interatomic distance is determined by random atomic movement.
Conclusions
We have theoretically investigated the effects of random discrete distribution of implanted and annealed As atoms in the S/D extensions on the device characteristics of ntype GAA Si NW transistors. KMC simulation is used for generating realistic random distribution of active As atoms in Si NWs, and the current–voltage characteristics are calculated using the NEGF method. The fluctuation of drain current is observed with the normalized standard deviation of approximately 0.2. The correlation between the drain current and the factors related to random As distribution is examined. The results indicate that the oncurrent fluctuation is not directly due to the fluctuation of the number of dopants in the S/D extensions. The oncurrent fluctuation may be caused by the randomness of As dopant positions in the S/D extensions and hence is inherent in ultrasmall NW transistors.
Abbreviations
 GAA:

gateallaround
 KMC:

kinetic Monte Carlo
 MOSFET:

metaloxide semiconductor fieldeffect transistors
 NEFG:

nonequilibrium Green's function
 NW:

nanowire
 RDD:

random discrete dopant
 S/D:

source and drain.
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Acknowledgments
We acknowledge Dr. Ignacio Martin Bragado for the fruitful discussions on KMC modeling.
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The authors declare that they have no competing interests.
Authors’ contributions
MU carried out the KMC calculations to obtain random discrete As distributions in the S/D extensions of NW transistors and drafted the manuscript. KMI supervised the KMC simulation. GM and HM participated in the NEGF simulation of NW transistors. NM carried out the NEGF calculations and analyzed the IV characteristics of NW transistors. All authors read and approved the final manuscript.
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Keywords
 Silicon nanowires
 Random discrete dopant distribution
 Gateallaround transistors
 Kinetic Monte Carlo
 Nonequilibrium green's function