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Characterization of Single Defects in Ultra-Scaled MoS Field-Effect Transistors Bernhard Stampfer, Feng Zhang, Yury Yuryevich Illarionov, Theresia Knobloch, Peng Wu, Michael Waltl, Alexander Grill, Joerg Appenzeller, and Tibor Grasser ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.8b00268 • Publication Date (Web): 07 Jun 2018 Downloaded from http://pubs.acs.org on June 12, 2018

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Characterization of Single Defects in Ultra-Scaled MoS2 Field-Effect Transistors Bernhard Stampfer,∗,† Feng Zhang,‡ Yury Yuryevich Illarionov,†,¶ Theresia Knobloch,† Peng Wu,‡ Michael Waltl,† Alexander Grill,† Joerg Appenzeller,∗,‡ and Tibor Grasser∗,† Institute for Microelectronics (TU Wien), Gusshausstrasse 27–29, 1040 Vienna, Austria, Purdue University, 1205 West State Street, West Lafayette, Indiana 47907, USA, and Ioffe Physical-Technical Institute, Polytechnicheskaya 26, 194021 St-Petersburg, Russia E-mail: [email protected]; [email protected]; [email protected]

Abstract

contain random telegraph noise, which is due to the transfer of charge between the channel of the transistors and individual defects, visible only due to the large impact of a single elementary charge on the local electrostatics in these small devices. Using hidden Markov models for statistical analysis, we extract the charge capture and emission times of a number of defects. By comparing the bias-dependence of the measured capture and emission times to the prediction of theoretical models, we provide simple rules to distinguish oxide traps from adsorbates on these back-gated devices. In addition, we give simple expressions to estimate the vertical and energetic positions of the defects. Using the methods presented in this work, it is possible to locate the sources of performance and reliability limitations in 2D devices and to probe defect distributions in oxide materials with 2D channel materials.

MoS2 has received a lot of attention lately as a semiconducting channel material for electronic devices, in part due to its large band gap as compared to other 2D materials. Yet, the performance and reliability of these devices is still severely limited by defects which act as traps for charge carriers, causing severely reduced mobilities, hysteresis and long-term drift. Despite their importance, these defects are only poorly understood. One fundamental problem in defect characterization is that due to the large defect concentration only their average response to bias changes can be measured. Based on such averaged data, a detailed analysis of their properties and identification of particular defect types is difficult. In order to overcome this limitation, we here characterize single defects on MoS2 devices by performing measurements on ultra-scaled transistors (∼65×50 nm) which contain only few defects. These single defects are characterized electrically at varying gate biases and temperatures. The measured currents

Keywords random telegraph noise, MoS2 , SiO2 , transistor, single defects, charge trapping, dichalcogenides

∗

To whom correspondence should be addressed † Institute for Microelectronics (TU Wien), Gusshausstrasse 27–29, 1040 Vienna, Austria ‡ Purdue University, 1205 West State Street, West Lafayette, Indiana 47907, USA ¶ Ioffe Physical-Technical Institute, Polytechnicheskaya 26, 194021 St-Petersburg, Russia

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Molybdenum disulfide (MoS2 ) is one of the most promising semiconducting transition metal dichalcogenides which is considered for applications in beyond-CMOS electronic devices. The direct electronic bandgap of singlelayer MoS2 can be as high as 2.6 eV, 1,2 which makes this material an interesting candidate for digital device applications. In addition, there is some understanding that MoS2 field-effect transistors (FETs) are suitable for high-frequency applications 3 and circuit integration. 4–6 In particular, numerous realizations of MoS2 FETs can be found in recent literature reports. 7–14 However, these studies mostly deal with the analysis of the performance of these devices, as well as the exploration of fabrication techniques allowing them to realize their theoretical performance potential predicted by simulations. 15 One of the most important performance limitations is due to defects which can arise from both non-optimized device processing and fundamental properties of insulators with their unique defect bands. 16–18 These defects can exchange charges with the channel, thus strongly affecting the device performance and reliability. 18,19 Although these problems are not often discussed in the current 2D literature, they are of utmost importance for industrial integration of these new technologies and the transition from device prototypes to commercial 2D devices. The reliability of previously investigated MoS2 FETs, as well as of 2D devices in general, is typically reduced by charge trapping in oxide traps 20–23 with very broad distributions of time constants 24 and trapping sites on top of the channel, e.g. adsorbates and water molecules. 19,25 Charge trapping decreases the mobility, and results in a hysteresis of the gate transfer characteristics, 18,19,25–28 as well as in partially recoverable threshold voltage shifts which appear whenever a bias is applied to the gate, 10,19,29,30 conventionally known as bias-temperature instabilities (BTI). 31–36 Although some understanding of these issues for different 2D technologies has been achieved in our previous works, 18–20,23 all these studies have been performed on large area device prototypes with micrometer dimensions. Since the

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defect densities in these devices are considerable (we obtained values of ∼1012 cm−2 in our studies mentioned before, which corresponds to 106 traps for e.g. a 10 µm × 10 µm device), the observed drifts of the transistor characteristics are the collective response of a large number of defects. However, a considerably improved understanding of the physical mechanisms of charge trapping and the nature of the involved traps can be obtained via the characterization of individual defects. Such a characterization is possible in ultra-scaled devices which contain merely a few defects within the channel area. 36 As the channel area of a device is reduced, the number of defects in the devices decreases, but at the same time their individual influence on the channel of the device increases. 37 This leads to an increase in the noise level of the drain current as compared to bigger devices. In particular, charging and discharging of single defects can be observed as steps in the drain current. This issue is known as random telegraph noise (RTN) 38 and can be observed only in scaled devices. Statistical characterization of RTN signals caused by every particular defect allows to extract the capture and emission time constants (τc and τe , respectively), trap level, activation energy and vertical position of this defect, 39 thus giving insights into the understanding of the microscopic defect properties. Typically, RTN analysis is possible if only a handful of defects are present. This implies that for a given defect density D, the device area has to be scaled A = 5/D. Based on our previous studies, where we extracted a D of about 1012 /cm2 , an area of 50 nm × 50 nm would be required to reach this single defect limit (see Figure S1 in the Supporting Information (SI)). Therefore we fabricated MoS2 FETs and report sub-100 nm devices which contain only individual discrete defects within the channel area. By performing statistical analysis of a number of RTN traces measured on different devices, we extract defect levels and vertical positions of these defects and discriminate between oxide traps and adsorbates on top of the MoS2 channel. This presents a considerable break-


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increases as the energy level of the defect shifts below the Fermi level. Once in its charged state, the defect causes a threshold voltage shift due to the presence of the electric charge close to the channel, affecting its conductivity. From silicon devices it is known that defects are also visible in stationary drain current measurements if they are close to the Fermi level at the applied gate bias. As such a defect randomly charges and discharges, the measured drain current shows discrete steps to a lower or higher value respectively, an effect called RTN. 38 A number of RTN traces with defects behaving in a different manner are shown in Figure 2bd. The time constants of the defects depend on their energetic and spatial position relative to the channel, the temperature, and the number of carriers in the channel. Thus, extracting their time constants under various conditions allows us to acquire important information on the defects and their surroundings. In Figure 2b we show a defect with time constants that are independent on gate bias. In contrast, Figure 2c shows a defect with a strong gate bias dependence. At a gate bias of −5 V, the bias dependent defect is mostly unoccupied, as indicated by the higher current state. Already at −4 V, the drain current is mostly in the low state, as the average capture time decreases and the emission time increases. The bias range where the defect shows RTN corresponds to the bias range where trapping is visible in the ID –VG measurements, although the range might be shifted depending on the sweep rate and direction of the ID –VG measurement. This microscopic behavior is what is visible in larger devices as hysteresis, caused by the superpositioned effects of many different traps. A more complicated case called anomalous RTN (aRTN) is shown in Figure 2d, where normal RTN behavior is interrupted by random periods of inactivity.

extract the capture and emission time for each bias point is to measure the duration of the high- and low-current states, and average the recorded times as indicated in Figure 2b. This method, however, is quite susceptible to the noise observed on the very low currents measured and typically only successful for simple RTN signals with a single active trap. To extract the time constants of the defects in our more complicated signals, we used hidden Markov models (HMMs) and the BaumWelch algorithm, which are commonly used also in speech processing and the analysis of DNA. 43 This method uses a Markov chain as a model for the defect, as shown in the insets of Figure 2b and d. The algorithm then iteratively optimizes the time constants of the model to most likely represent the measured data (see the methods section for details). This allowed us to extract the time constants of a number of defects. Four representative examples, termed A to D, are shown in Figure 3: Three of the defects (A, C, D) showed normal RTN behavior which can be described by a two-state Markov model. In contrast, defect B showed random periods of inactivity (Figure 2d), indicating that it has an additional meta-stable state between the stable charged and discharged states. This is called aRTN and is known from measurements in Si/SiO2 devices. 44 Anomalous RTN is usually explained by a configurational change at the defect site. 36 Of the four defects presented, two show a bias dependence of their characteristic capture and emission times, while the other two exhibit no gate bias dependence. The latter is not observed in Si/SiO2 devices and most likely due to adsorbates on top of the device. For the interpretation of the gate bias dependence we start with a trap located in the oxide between the backgate and the channel (i.e. an oxide defect) or just under the channel (dangling bonds or processing residue). Applying a gate bias will result in a change of the energetic position of the trap relative to the energies of the carriers in the channel, which will in turn affect the time constants. A defect located within the MoS2 (e.g. an S vacancy), at the edge of the channel (e.g. etching induced

Extraction of Time Constants In order to gain more insight into the physical properties of the defects, it is necessary to extract the time constants from the measured traces. The most straight forward method to


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Table 1: Extracted trap parameters, calculated with the analytical expressions 1 and 2 (left), and from TCAD device simulation (right). Energies are relative to the conduction band edge Type Defect 16 . 25 26 28 a

(A)b (B) (C) (D)

Approximation E1′ ,approx zapprox 0.003 eV . 0.010 eV .−0.018 eV −0.843 eV

−0.8 nm ≈8.0 nm ≈8.0 nm −3.2 nm

E1 a

ε1 ′ 1 a

— −0.149 eV — —

— 0.99 eV — —

Fit Parameter E 1′ R1 ′ 2 — −0.017 eV −0.016 eV −0.640 eV

— 0.80 0.69 0.52

S1′ 2 ~ω — 1.60 1.38 0.33

zsimulated — 8.5 nm 8.0 nm −1.7 nm

Parameters for three-state defects only b No TCAD fit

Ar: 10 sccm; pressure: 3 Pa; RF source power: 50 W; RF bias power: 50 W; time: 17 s). Next, Ni (65 nm) electrodes were deposited acting as source/drain contacts. This step defined the channel length (L ≈ 50 nm) of the back gated three-terminal structures. Flake thicknesses varied between 5 nm and 15 nm.

drain current in dynamic (ID –VG ) and static (RTN) measurements, and extracted time constants from the measured traces. We found that part of the defects in these devices show time constants independent of gate bias, unlike the defects commonly found in silicon devices. We argue that this is likely due to the position of these defects in the devices, and give equations to estimate spatial and energetic positions. We compared the Shockley-Read-Hall and non-radiative multi phonon trap models to find that the simple SRH model is unable to explain the temperature and bias dependences of the measured data and – just like in Si technologies – the NMP model should be used. Using detailed TCAD simulations we have extracted physical parameter sets of the measured defects. From the obtained defect parameters, we made claims as to the physical nature of the studied defects. Using the presented methods, it is possible to pinpoint defects in 2D-devices, and thus learn more about the feasibility of certain oxide and channel material combinations for microelectronic devices.

Measurement Basic electrical characterization (Figure 1bc) of the devices was performed at roomtemperature in air using a commercial parameter analyzer (Agilent 4156C). RTN measurements were performed in the vacuum chamber of a LakeShore probestation which is able to reach a minimum pressure of 5 × 10−6 torr. The vacuum chamber was connected to a dewar with liquid nitrogen, while the temperature was varied between 100 K and 373 K and controlled by a LakeShore temperature controller. Electrical defect characterization was performed using measurement equipment built at TU Wien, which allows to perform a wide range of current measurements down to the microsecond range. 50 Depending on the time constants of the observed defects, measurement time intervals for each single trace were varied from 0.1 s for faster traps to 1000 s for slower traps, with the number of measurement points adjusted accordingly. In order to obtain enough data for statistical analysis, at each bias point we measured 50 – 100 traces for smaller time intervals and 5 – 20 traces for larger time intervals. Where possible, defects were characterized at different temperatures, typically ranging within an 40 – 80 K interval.

Methods Fabrication MoS2 (SPI Supplies) flakes were exfoliated onto a 20 nm silicon dioxide (SiO2 ) substrate with underlying highly doped silicon using standard scotch tape techniques. The 20 nm SiO2 layer was used in the following as the gate dielectric. After the flake transfer, e-beam lithography was used to define the channel width (W ≈ 65 nm) of the active device region, followed by plasma dry etching (SF6 : 10 sccm;


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Parameter extraction

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ting parameters. Following this, we used the NMP trap model together with calibrated device structures to perform the single-trap simulations of the capture and emission times. Further details of the simulations are discussed in the supporting information.

To extract the capture and emission times of individual defects from the recorded data, we statistically analyzed traces in the bias range where RTN was visible. For each bias point and temperature we trained HMMs composed of two- or three state traps to the traces with software developed at TU Wien using the BaumWelch algorithm. 43 The Baum-Welch algorithm is used on the drain current measurements to give the most likely set of parameters (transition times, step heights) for the measured data given the Markov model of the trap. The Markov model itself consists of a number of nodes (the hidden states of the defect, e.g. 1, 1′ , 2), the allowed transitions between these states (1 ⇌ 1′ ⇌ 2) and the observables of the hidden states (e.g. states 1, 1′ → high current level, state 2 → low current level, measurement noise σ).

Acknowledgement The authors thank for the financial support through the FWF grant n◦ I2606-N30. Supporting Information Available: A PDF file is available with additional information on: Single-defect limit for different 2D technologies; SEM image of a device; Dependence of capture and emission times on trap position; Device modeling; Adsorbates on the MoS2 surface; Estimation of trap position and energy; Detailed results for individual defects; This material is available free of charge via the Internet at http://pubs.acs.org/.

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