Two-dimensional (2D) semiconductors are a potential channel material for ultimately scaled field-effect transistors (FETs)1. In contrast to silicon, 2D semiconductors retain sizable mobilities at atomic layer thicknesses below 1 nm, a thickness that also helps to suppress short-channel effects in FETs and thus allows for physical channel lengths below 5 nm (ref. 2). Furthermore, the integration of 2D materials in van der Waals heterostructures provides design options for energy-efficient transistors that can overcome the limitations of thermal charge-carrier injection. In addition, 2D materials are of potential use in a broad range of applications, including photonics and optoelectronics3, neuromorphic computing4, nanoelectromechanical systems5, and gas and biological sensors6.

There is, however, currently a lack of low-resistive contacts to 2D semiconductors, which minimize the prevalent Schottky barriers7. In addition, there is a lack of suitable gate insulators8 that can ensure high interface quality, scalability9 and a minimum of electrically active border traps, the presence of which in the insulator close to the channel limits device stability. These two challenges are major obstacles for the industrial applications of 2D-based nanoelectronics and, being independent from each other, need to be separately addressed. There has been recent progress regarding the formation of low-barrier contacts for 2D semiconductors using contact gating10 or semimetallic bismuth contacts to achieve ultralow contact resistances11. However, the need to find a suitable insulator with a minimum number of electrically active traps remains. Stability studies of 2D FETs typically show a stability that is at least two orders of magnitude worse12,13 compared with silicon-based FETs14,15.

Measurements of FET stability typically evaluate the hysteresis in the transfer characteristics16 and stability of threshold voltage under prolonged periods of applied elevated gate biases and temperatures (bias temperature instability (BTI))14. Charge trapping inside the gate oxide has been identified as the root cause of BTI15,17. At elevated gate biases and temperatures, charges are transferred between the channel and gate oxide in a phonon-mediated transition18, with charging time constants spanning a wide range, from picoseconds to years2,19. Border traps in the gate oxide close to the channel determine the long-term stability and reliability of silicon FETs15, whereas in 2D-material-based FETs, they typically limit device stability on much shorter timescales20.

In amorphous oxides, the defect trap levels vary due to the differing surroundings of every instance of atomic defects18. In the first approximation, the energy levels of defects follow a normal distribution around the average defect levels, forming defect bands21. Consequently, the overall density of border traps and the widths of the corresponding defect bands can be considerably reduced by using crystalline insulators, such as hexagonal boron nitride (hBN) or calcium fluoride8 (CaF2). However, these insulators are difficult to synthesize and have several technological challenges. For example, current state-of-the-art crystalline hBN can be grown only at temperatures above 1,200 °C (ref. 22) and CaF2 requires a crystalline silicon(111) substrate for growth, allowing only back-gated configurations23. In addition, hBN is unsuitable for use as a scaled gate insulator because of its small dielectric constant9.

In this Article, we show that the stability and reliability of 2D-material-based FETs with amorphous gate oxides can be improved by tuning the Fermi level (EF) of the 2D channel material such that it maximizes the energy distance between the charge carriers in the channel and the defect bands in the gate insulator during device operation. This can be achieved via a careful selection of the 2D material and amorphous gate oxide, as well as by doping the 2D layer to shift EF away from the defect bands in the gate insulator. Graphene FETs (GFETs) with aluminium oxide (Al2O3) as the top-gate oxide are measured and compared, where one of the device batches uses a p-doped graphene layer. The GFET batch where EF is tuned away from the Al2O3 defect band edge shows reduced hysteresis and BTI. We further verify the approach with technology computer-aided design (TCAD) simulations24. In addition, double-gated GFETs are fabricated, where the back gate is used to directly tune the Fermi level in the graphene monolayer via electrostatic doping. This shows that the stability observed for top-gate operation strongly depends on the location of EF. Our approach aims to design a metal–oxide–semiconductor (MOS) system with a minimal amount of electrically active border traps without the need to reduce the total number of traps in the insulator.

Fermi-level tuning for increasing stability of 2D FETs

Our stability-based design approach is centred on the analysis and design of the band diagram of the MOS system, including the defect bands in the insulator. Fig. 1a shows a top-gated GFET that forms an example MOS system out of aluminium (metal), Al2O3 (oxide) and graphene (semiconductor). The corresponding band diagram of a cut through the MOS stack (Fig. 1a, left, indicated by arrow) is shown on the right. Every material is characterized in this view by its electron affinity and thus the energetic distance of the conduction band edge to the vacuum level, as well as its bandgap. In the case of metals and semi-metals, the work function, the energetic distance of EF to the vacuum level, determines the energetic location of charge carriers. In this regard, we use the Schottky–Mott rule to determine the band alignments shown as a zero-order approximation, thereby neglecting interface-specific reactions and charge imbalances that would lead to additional shifts on the order of a few hundred millielectronvolts25. Knowledge of the energetic position of the oxide’s defect bands and alignment to EF is the core of our design approach.

Fig. 1: Fermi-level tuning to maximize the energetic distance to oxide defect bands.
figure 1

a, Schematic (left) shows a top-gated GFET with an Al2O3 gate oxide. For a cut through the GFET along the indicated arrow, the energetic alignment of the Fermi level to the defect band in the aluminium gate oxide is shown. In the band diagram (left), the device is electrically unstable with respect to variations in the threshold voltage as the Fermi level is aligned within the defect band. In the band diagram (right), the Fermi level has been shifted downwards, rendering the device more stable. b, Schematic of the charge transfer of electrons flowing through the WS2 channel to traps in the HfO2 gate oxide (left). This situation is depicted in the left band diagram where the Fermi level is aligned close to the conduction band edge, rendering the device unstable. If the Fermi level is instead aligned close to the valence band edge, the FET is stable. c, In this band diagram, the possible range of the graphene Fermi levels, which is currently achievable by doping, is shown as a grey-shaded region. The Fermi level can be continuously tuned within this region. d, Injection of electrons and holes from the band edges of WS2. In a layered semiconductor, the number of layers modifies the bandgap and doping determines whether electrons or holes will be the majority carriers and thus govern device stability.

The energetic position of defect bands in amorphous oxides is an intrinsic material property26,27, as defect bands are related to certain defective atomic configurations inside the amorphous material, which result in slightly varying trap levels depending on the local surroundings of the defects. In effect, the superposition of the trap levels of many atomic defects forms the defect band, characterized by the average energetic trap level \(\overline{{E}_{{{{\rm{T}}}}}}\) and the standard deviation of the trap level distribution \({\sigma }_{{{{{{E}}}}}_{{{{\rm{T}}}}}}\). To experimentally determine the energetic location of the defect bands, the oxide defect states can be probed by electrical measurements, which analyse conductance variations in MOS systems28,29, or by electron paramagnetic resonance measurements, which detect the magnetic moment of unpaired electrons30. Defect bands can be theoretically determined using ab initio calculations where possible defect states and their prevalence are analysed, thereby identifying electrically active defect configurations such as oxygen vacancies31 or hydrogen-related defects32. Currently, the energetic locations of oxide defect bands are known for amorphous SiO2 (ref. 29), HfO2 (refs. 26,31) and Al2O3 (ref. 28) insulators. Besides the location of the defect bands, another essential property of insulator traps is their extremely broad distribution of time constants, ranging from the picoseconds regime up to years2,19. As these insulator traps lead to noise, hysteresis and drifts, it is important to thoroughly characterize both traps’ time constants and their energetic location within the defect bands.

Based on the band alignment of the graphene work function to the defect bands in Al2O3, we can predict the electrical stability of the threshold voltage in these FETs. In Fig. 1a (left band diagram), the work function of graphene is shown to be at 3.9 eV, which corresponds to n-doped graphene33. This graphene layer’s EF lies in the middle of the Al2O3 defect band. Due to this alignment within the defect band, charge traps in the oxide capture and frequently emit charges. As the applied gate voltage modifies the charging probabilities of the defects according to the electric field19, VTH depends on the biasing history and a pronounced hysteresis is visible. In addition, VTH drifts considerably during prolonged periods of applied gate biases.

However, the theoretical considerations of Fig. 1a suggest that FET stability can be tuned by moving EF down by p doping the graphene layer. Here the EF value of graphene of 5.1 eV can be achieved through p doping34. As the graphene Fermi level is located below the Al2O3 defect band, charge transfer is unlikely. Therefore, the oxide defects are electrically inactive, resulting in stable VTH throughout device operation, independent of the applied biases. In graphene, doping with different adsorbates and substrates results in a quasi-continuous variation in the Fermi level between 3.4 and 5.1 eV (refs. 35,36), which can be used to tune the Fermi level during device design to minimize the impact of oxide defect bands.

With some adaptation, the same stability-based design process can be applied to enhance the stability in FETs based on 2D semiconductors; Fig. 1b shows the schematic of a WS2 FET with HfO2 top-gate oxide. If the Fermi level is aligned close to the conduction band, electrons within WS2 are the majority charge carriers dominating the current flow in Schottky-barrier FETs7. As the conduction band edge of WS2 is aligned with the electron-trapping defect band of HfO2, charge transfer to oxide defects is frequent. If WS2 was p doped instead of n doped, holes at the valence band edge would be the majority (Fig. 1b, right band diagram). As the valence band edge of WS2 is located below the hole-trapping band in HfO2, the charging of oxide defects is highly unlikely. Therefore, for p-doped WS2 in combination with HfO2 gate oxide, there are no electrically active oxide traps, leading to a stable VTH during device operation. It should be noted that for 2D semiconductors, the charges are always injected from the conduction or valence band edges. Thus, when designing a stable n-type or p-type MOSFET, a suitable combination of 2D semiconductor and oxide needs to be selected. In this context, a recent study has suggested that Fermi-level pinning in MoS2 is weaker if the oxide defect bands can be avoided. It was observed that the degree of Fermi-level pinning is reduced when using Al2O3, which possesses no oxide defect band in the vicinity of the valence band edge of MoS2, instead of SiO2 in back-gated FETs37.

The physical possibilities for tuning the stability in the context of stability-aware device design are illustrated in Fig. 1c,d. By doping the graphene layer, graphene’s EF can be tuned within the entire grey-shaded area (Fig. 1c). Thus, the design freedom for a stability-based device is large in graphene FETs; furthermore, the role of SiO2 defect bands can be reduced with an EF alignment in the middle of the two defect bands, whereas the impact of the Al2O3 defect band can be minimized when using p-doped graphene layers. For 2D semiconductors like WS2, the freedom for stability-aware design is smaller. Fig. 1d shows that either the conduction or valence band edge can be chosen via doping. However, n-type WS2 presumably will be electrically unstable for the amorphous oxides investigated here, whereas stable p-type FETs could be designed using Al2O3 or HfO2.

It is worth noting that several studies have reported high densities of fixed charges at the interfaces of 2D materials with amorphous oxides, for example, MoS2/SiO2 (refs. 25,38), WS2/SiO2 (ref. 39) or graphene/SiO2 (ref. 40). This evidence suggests that there might be a loss of charge neutrality at the ill-defined interfaces between van der Waals–bonded 2D layers and amorphous oxides, causing deviations from the Schottky–Mott rule25. These deviations result in offsets to the band alignments that can be determined, for example, with internal photoemission measurements41 or scanning probe techniques42. Although these offsets would need to be taken into account for optimum matching of the Fermi level at a maximum distance to the oxide defect bands, they are neglected for the proof-of-concept study presented here.

At the same time, the intentional placement of charges at the interface could be used to shift the band edges away from the oxide defect bands using, for example, surface charge transfer doping43. However, fixed charges at the interfaces would also degrade the mobility in the semiconducting 2D channel25. This could be avoided by using more complex gate stacks with electric dipoles at the interfaces between different oxides. Such a dipole engineering approach has been successfully used to improve the reliability of silicon FETs with an HfO2/SiO2 gate stack21,44.

To estimate the electrical stability improvement that can be achieved by Fermi-level tuning in FETs with amorphous oxides, we simulated the hysteresis width in FETs based on 2D semiconductors in relation to the location of the conduction band edge, ECB. For simulations, we used the previously developed drift-diffusion-based TCAD methodology45 coupled to a non-radiative multiphonon model18 (Supplementary Section 1). In Fig. 2, we calculated the hysteresis width in a model system of monolayer MoS2 with a back-gate oxide of SiO2 (ref. 45). We evaluated the hysteresis width at VTH, defined here as the voltage where the Fermi level is located –0.05 eV below the conduction band edge (Fig. 2a). Based on the criterion for EF − ECB, a constant-current criterion was defined and the hysteresis width was evaluated as a function of varying distance of the trap level \(\overline{{E}_{{{{\rm{T}}}}}}\) to ECB. For an oxide defect-band width of \({\sigma }_{{{{{{E}}}}}_{{{{\rm{T}}}}}}\) = 0.3 eV, the hysteresis width can be reduced by half an order of magnitude if the conduction band edge is shifted 350 meV downwards, as illustrated in the band diagrams in Fig. 2c. These shifts in the conduction or valence band edges can be achieved, for example, by transitioning from monolayers to bulk material (Fig. 1d). For example, in WS2, the conduction and valence band edges shift by approximately 160 meV when using bilayers instead of monolayers, or by about 370 meV when using bulk WS2 (ref. 46). Thus, we would expect that n-type WS2 FETs with an HfO2 gate oxide are more stable when using bulk WS2 as a channel compared with thinner WS2 layers. In cases where an ultimately thin monolayer channel is required, electrically stable FETs could be designed by choosing a different combination of 2D semiconductor and insulator. For example, increased electrical stability is predicted for BP/HfO2 FETs and for ZrSe2/Al2O3 FETs (Extended Data Fig. 1 and Supplementary Section 2).

Fig. 2: Estimation of stability improvement based on TCAD simulations.
figure 2

a, Calculated distance of the MoS2 Fermi level to its conduction band edge (top). The hysteresis width ΔVH is extracted at the threshold voltage, defined as EF being located 50 meV below the conduction band edge. From the simulated transfer characteristics of the MoS2 FETs based on SiO2 (bottom), the constant-current criterion of Icrit = 4.8 × 10−5 μA μm−1 was used to evaluate ΔVH. b, Hysteresis width ΔVH is shown on a logarithmic scale as a function of the distance of the oxide trap level \(\overline{{E}_{{{{\rm{T}}}}}}\) to the MoS2 conduction band edge EC. If EC is moved 375 meV down, away from the trap band, the hysteresis width improves by a factor of 5. c, At two different locations of EC, namely, at \(\overline{{E}_{{{{\rm{T}}}}}}-{E}_{{{{\rm{C}}}}}\) = 0.100 eV in dark blue and 0.475 eV in light blue corresponding to the colours of the dotted lines in b, the band diagrams of the MoS2/SiO2 system are shown, demonstrating how fewer oxide traps change their charge state if the conduction band edge is shifted down, leading to a reduction in the hysteresis width by half an order of magnitude.

It should be noted that for narrower defect bands, the improvement accessible by tuning the semiconductor band edges is much larger. For example, we repeated the calculations shown in Fig. 2 for an insulator defect band of only 0.07 eV. In this example, the hysteresis width is reduced by one order of magnitude by shifting the conduction band edge by 82 meV (Extended Data Fig. 2 and Supplementary Section 3). Such a reduction in the widths of the defect bands is expected for crystalline gate insulators, such as hBN or CaF2 (ref. 8). Independently, graphene, with its continuous tunability of EF over an interval of nearly 2 eV, provides the largest design freedom. Due to the possibility to tune the Fermi level in graphene by a few 100 meV through moderate doping, we chose graphene/Al2O3 as a model system to experimentally verify our stability-based design approach.

Graphene Fermi level and Al2O3 defect bands

To test our proposed stability-based design, we fabricated two batches of GFETs using graphene samples with different doping levels, termed as Type 1 graphene and Type 2 graphene. In addition, we fabricated GFETs with a double-gated structure where the back gate can be used to electrostatically dope the graphene channel47,48. In the first two batches, graphene monolayers form a channel with an area of W × L = 100 μm × 160 μm on top of mechanically flexible polyimide (PI) substrates49 (Fig. 3a). In the top-gated device layout, a 40-nm-thick amorphous Al2O3 layer, grown by atomic layer deposition, is used as the gate oxide. The two fabricated GFET batches using Type 1 and Type 2 graphene mainly differ in the respective doping and quality of their graphene channels. These graphene layers were purchased from different vendors using different parameters for the chemical vapour deposition (CVD) process and layer transfer. Type 1 graphene exhibits a work function that results in a small distance of EF to the Al2O3 trap band (\(\overline{{E}_{{{{\rm{T}}}}}}\)). According to our theory, this small value of \(\overline{{E}_{{{{\rm{T}}}}}}-{E}_{{{{\rm{F}}}}}\) predicts electrically unstable devices. In contrast, Type 2 graphene is p doped with a higher distance of EF to \(\overline{{E}_{{{{\rm{T}}}}}}\), predicting electrically more stable FETs. Furthermore, the graphene films have vastly differing qualities, with Type 2 graphene exhibiting a higher concentration of defects (Extended Data Fig. 3 and Supplementary Section 4 show the respective Raman spectra). Although we anticipate that a higher defect concentration would lead to an overall degraded GFET performance, if our hypothesis is correct, the p doping of Type 2 devices should nevertheless lead to more stable devices due to the larger distance of EF from the defect band.

Fig. 3: GFET design and performance.
figure 3

a, Schematic showing the cross section (left), and the optical microscopy image shows the top view of the device layout (right). S, source; D, drain. b,c, Output (IDVD) characteristics of a representative device of Type 1 (b) and transfer (IDVG) characteristics (c). Type 2 GFETs have the same layout as Type 1 GFETs, but are based on a different CVD-grown graphene layer from another vendor. d, Transfer (IDVG) characteristics of a representative device based on Type 2 graphene. Here only the forward sweeps are shown; the backward sweeps are reported in Fig. 5. e, Mean current and voltage at the Dirac point are compared for the Type 1 and Type 2 devices (VDirac,T1 and VDirac,T2, respectively). These values were calculated from the transfer characteristics measured on 50 different GFETs of every type using VD = 0.1 V, with every single measurement value given as a small dot. The large dots show the mean value and the bars denote the error bars, giving an error of 1σ. f, Cross section through the GFETs with a silicon wafer as the global back gate. g,h, For these double-gated GFETs, IDVTG curves recorded at tSW = 0.4 s are shown for VBG = 20 V (g) and VBG = –20 V (h) including the backward sweep (dashed lines).

To assess the functionality and performance of our GFETs, the output (IDVD) and transfer (IDVG) characteristics are shown for a representative Type 1 GFET (Fig. 3b,c). We observe ambipolar device operation with kinks in the output characteristics at higher VD, features typical for GFETs47. This local saturation of the output characteristics has been linked to a pinch-off region in the monolayer graphene channel, where the majority charge type changes and the charge concentration declines locally47. When we compare these characteristics with those of Type 2 graphene FETs (Fig. 3d), it is evident that the higher quality of Type 1 graphene leads to higher current densities. Based on two-probe measurements of the IDVG characteristics, we estimate the field-effect mobilities to reach up to 5,000 cm2 V−1 s−1 in Type 1 GFETs, four times the average mobility of about 600 cm2 V−1 s−1 in Type 2 GFETs. These results are expected based on the Raman analysis and originate from the higher amount of defects in Type 2 graphene.

Negatively charged dopants in Type 2 lead to higher variability and shift VDirac towards more positive voltages, as evident from the comparison of VDirac measured on 50 devices for each graphene type (Fig. 3e). More details on the variability of the two types of GFET studied here are provided in Extended Data Fig. 4 and Supplementary Section 5. A more positive VDirac corresponds to a higher p doping of the sample and correlates with a higher work function (EW)34. Pristine graphene has a work function of 4.56 eV (ref. 50), which is shifted towards higher values by p doping34 and towards smaller values by n doping33,35. To calculate the Fermi-level location in the two graphene types, we obtain the charge-carrier concentration (n) based on the analytic expression for n in the MOS capacitor36,51. At a top-gate bias of 0 V, we extract the charge-carrier concentration (n) caused by the intrinsic doping of graphene samples


with the total gate capacitance of the structure (Ctot) and elementary charge q. Here, Ctot is given by the capacitance of Al2O3 (Cox) in series with the capacitance of 0.5 nm van der Waals gap (CvdW) and the quantum capacitance of graphene52 (Cq), amounting to Ctot = 0.16 μF cm−2. This expression gives a p-doping density for Type 1 graphene of n1 = 5.5 × 1010 cm−2 and for Type 2 graphene of n2 = 2.8 × 1012 cm−2. Thus, Type 2 graphene is more p doped by an additional doping density of approximately 2.75 × 1012 cm−2. These hole densities in the graphene layers at 0 V gate voltage determine the work function via33,42

$${E}_{{{{\rm{W}}}}}=\hslash {\nu }_{{{{\rm{F}}}}}\sqrt{\uppi n},$$

where the Fermi velocity in graphene is νF = 1.1 × 106 m s−1 (ref. 53). Consequently, we obtain EW1 of Type 1 graphene to be 4.6 eV and EW2 of Type 2 graphene to be 4.8 eV, that is 0.2 eV higher (Supplementary Section 6 shows the calculation of the work function). For all the typical FET metrics, Type 2 graphene suggests poorer performance, including lower mobility and lower ON/OFF ratio. However, because EW2 is higher than EW1, our stability-based design theory suggests that Type 2 graphene should produce more stable GFETs, which is what we set out to prove below.

To further analyse our model system, we fabricated devices with Type 1 graphene but using thermal SiO2 on silicon and quartz substrates instead of a flexible PI layer. In addition, the quality of the interface between graphene and Al2O3 was modified by transferring single-layer CVD-grown hBN layers before the ALD deposition or by sputtering ~2-nm-thick aluminium as a seed layer for the Al2O3 growth process. As shown in Extended Data Fig. 5 and Supplementary Section 7, the substrate primarily impacts the maximum current density, whereas the quality of the interface with Al2O3 impacts device stability.

Furthermore, we fabricated double-gated GFETs using 90 nm SiO2 as a back-gate oxide and the silicon wafer as a global back gate (Fig. 3f). This configuration allows electrostatic control of the doping of the monolayer graphene channel via the back gate48. By applying a positive voltage at the back gate of, for example, 20 V (Fig. 3g), the Dirac voltage of the top gate is shifted towards more negative voltages, corresponding to a smaller work function of graphene. Conversely, VBG = –20 V makes VDirac of the top gate more positive and results in a higher graphene work function (Fig. 3h). Consequently, these devices are expected to be more electrically stable at higher negative VBG than at higher positive VBG, as shown below.

To accurately determine the alignment of EF in graphene to the electron-trapping band of the amorphous Al2O3 gate oxide at \(\overline{{E}_{{{{\mathrm{T}}}}}}\), knowing the precise location of the oxide defect band is essential. Several studies have investigated the alignment of this defect band using trap spectroscopy by charge injection and sensing (TSCIS)28,54, BTI55,56 and hysteresis measurements20. The defect band alignments of Al2O3 as obtained from the literature are shown in Fig. 4a, with the corresponding parameters listed in Supplementary Section 8. Based on density functional theory (DFT) calculations, this defect band can be associated with either oxygen vacancies57 or aluminium interstitials57. For our study, we use a normally distributed defect band with the mean defect level at EC − ET = 2.15 ± 0.30 eV below the conduction band edge of Al2O3. The electron affinity (χ) of Al2O3, which determines the location of the conduction band edge, varies in the literature. Here we use 1.96 eV, as obtained from internal photoemission measurements41. For all the measurement ranges used in our work, we only probe the lower part of a potentially wider defect band further up, as reported using other methods55. This is illustrated in Fig. 4b, where the regions that can be probed by measurements are shaded in red and yellow. These shaded regions reach the upper edge of the defect band used here, but cover only the lower part of the wider defect band reported elsewhere55. For Type 1 graphene, EF is aligned within the defect band (small \(\overline{{E}_{{{{\rm{T}}}}}}-{E}_{{{{\rm{F}}}}}\), electrically unstable) (Fig. 4c), whereas for Type 2 graphene, it is aligned below the defect band (high \(\overline{{E}_{{{{\rm{T}}}}}}-{E}_{{{{\rm{F}}}}}\), electrically stable) (Fig. 4d). Below, we discuss that as proposed above, the 200 meV downward shift of the Fermi level of Type 2 graphene is sufficient to make the VDirac value of these GFETs more electrically stable.

Fig. 4: Defect band alignment in Al2O3.
figure 4

a, Band diagram illustrates the alignment of the Al2O3 defect band to Type 1 and Type 2 graphene. The location of defect bands as extracted from experiments is shown: (1) (ref. 28), (2) (ref. 54), (3) (ref. 55), (4) (ref. 56) and (5) (ref. 20). Also, the alignment of the defect band caused by oxygen vacancies and Al interstitials in amorphous Al2O3 is shown according to DFT calculations (6) (ref. 57). b, Active region probed by measurements in the [–5, 5 V] and [–10, 10 V] range is shown for two defect band alignments for Type 1 GFETs. c, Schematic of the band diagrams showing the charging and discharging of defects in Al2O3 for Type 1 graphene with a work function of EW = 4.6 eV—a value that can be qualitatively reached also with VBG ≥ +20 V. d, Band diagrams for Type 2 graphene with EW = 4.8 eV are shown, an effective doping level qualitatively accessible with VBG ≤ –20 V.

Hysteresis dynamics of GFETs

We first compare the double-sweep transfer characteristics for a small voltage range of [–5, 5 V] on five GFETs based on Type 1 graphene (Fig. 5a). We note little variability, which is confirmed when studying the hysteresis width ΔVH as a function of the inverse sweep time (tSW), namely, the sweep frequency (f = 1/tSW). In Fig. 5b, the hysteresis width as a function of the sweep frequency is shown for five GFETs based on Type 1 graphene and five GFETs based on Type 2 graphene. Type 2 devices show a considerably higher variability of ΔVH than Type 1 devices, which is linked to the increased variability of VDirac on Type 2 (Fig. 3e). In addition, on Type 2 GFETs, the hysteresis is higher; for both types, the largest hysteresis is observed for the slowest sweeps as the largest number of oxide defects can change their charge state20. Since the observed hysteresis critically depends on the voltage ranges used for the gate-voltage sweeps, we compare the bias ranges used with ranges for various applications (Supplementary Section 9), concluding that the gate-oxide fields investigated here are standard operating conditions for radio-frequency applications.

Fig. 5: Hysteresis in the transfer characteristics of GFETs.
figure 5

a, Hysteresis in the transfer characteristic measured on five different Type 1 GFETs, illustrating the variability of the devices. b, Hysteresis width as a function of 1/tSW is shown for five different devices for each graphene type. The circles and triangles are used to represent Type 1 and Type 2 GFETs, respectively, whereas the solid and dashed lines are guides to the eye for Type 1 and Type 2 GFETs, respectively. c, Hysteresis width for Type 1 and Type 2 GFETs is shown as measured on one representative GFET per type. d, Dirac-point shifts of the up and down sweeps are shown with empty/full symbols for VDirac,down/VDirac,up, respectively. e,f, For double-gated GFETs, the IDVG curves at slow sweeps are compared for two different VBG values (e) with the respective hysteresis widths as a function of the inverse sweep time (f). g, Extent of doping the GFET as a function of the back gate voltage, for various sweep times, based on VDirac and equations (1) and (2). h, Relative change in the hysteresis width for the slowest sweeps is shown for different measurement rounds on two GFETs (D1 and D2) at varying VBG values, illustrating that the hysteresis width increases for higher VBG. i, Respective effective electrostatic doping for the measurement rounds on D1 and D2.

An increased bias range of [–10, 10 V] increases the hysteresis, because more oxide defects become accessible for charge transfer (Fig. 5c) for the representative Type 1 and Type 2 GFETs. To shed more light on this behaviour, the dynamics of the Dirac voltage shifts are analysed as a function of the sweep frequency (Fig. 5c). For the [–5, 5 V] sweep, VDirac,up and VDirac,down as a function of the sweep frequency show similar slopes for both types. However, for the 10 V sweep range and Type 1 GFET, VDirac,up is shifted to more negative voltages in slow sweeps, whereas VDirac,down is shifted to more positive voltages. This indicates that for large sweep ranges on Type 1 GFETs, a large amount of electrons are emitted from the oxide traps between –10 V and VDirac,up, whereas for Type 2 GFETs, charge trapping can be neglected in this interval. This reversed drift of VDirac,up to more negative voltages at slower sweeps results in an increase in the hysteresis width in Type 1 GFETs (Fig. 5c). The increased hysteresis at large sweep ranges for Type 1 GFETs confirms our hypothesis that as the EF value of Type 1 GFET is located closer to the Al2O3 defect band, the GFETs are electrically less stable.

The band alignments shown in Fig. 4c,d qualitatively explain the larger hysteresis in Type 1 GFETs compared with Type 2: in Type 1 GFETs biased at VDirac, a considerable number of defects are negatively charged. If a negative voltage is applied, these defects discharge due to band bending, and thus, VDirac is shifted to more negative voltages during a slow up-sweep (Fig. 5c). In contrast, in Type 2 GFETs, the Fermi level is located below the defect band at VDirac, as its Fermi level has been shifted down by 200 meV via p doping. Thus, most defects are neutral at the Dirac voltage. If a long time is spent with the GFET biased at negative voltages, the charge states do not change and the location of VDirac during the up-sweep is stable, independent of the sweep time.

In summary, the higher \(\overline{{E}_{{{{\rm{T}}}}}}-{E}_{{{{\rm{F}}}}}\) of Type 2 graphene with respect to the Al2O3 defect band leads to a smaller hysteresis width for large sweep ranges. At small gate-bias ranges and fast hysteresis sweeps, Type 2 devices suffer from more charge trapping at the defective interface with the Al2O3 insulator, and the hysteresis is similar or even higher in Type 2 devices compared with Type 1 devices (Fig. 5b, Extended Data Fig. 6 and Supplementary Section 10). For fast sweeps, fast traps at the defective interface in Type 2 GFETs increase the hysteresis, giving the impression of a frequency-independent hysteresis width (Fig. 5c). Type 1 GFETs exhibit a cleaner interface but a smaller \(\overline{{E}_{{{{\rm{T}}}}}}-{E}_{{{{\rm{F}}}}}\) with respect to Al2O3 defects, strongly degrading the GFETs during slow sweeps. For high gate-bias ranges and slow sweeps, the border traps of Al2O3 dominate the device stability, thus more stable operation of Type 2 GFETs is observed.

In the double-gated configuration, the graphene layer can be dynamically doped in situ (Fig. 3f). To determine the impact of electrostatic back-gate doping on the top-gate stability, we characterized the hysteresis in the top-gate ID (VTG) curves after biasing the devices at a static VBG. Subsequently, the top-gate hysteresis was measured at different sweep rates. In Fig. 5e, the hysteresis at the top gate is shown for slow sweeps and various back-gate voltages from 12 V down to –40 V. When comparing the hysteresis widths as a function of the sweep time and the applied back-gate voltage (Fig. 5f), two trends are clearly observed. First, the hysteresis is reduced for fast sweeps; second, the hysteresis is the smallest for the most negative VBG. It is expected that hysteresis can be reduced for high negative back-gate voltages, as the work function of the graphene channel is the highest at 4.3 eV for a higher negative VBG (Fig. 5g). The work function was calculated based on the measured VDirac as a function of VBG and equations (1) and (2). At high graphene work functions, the Fermi level is located closer towards the lower edge of the defect band in the Al2O3 top-gate oxide, reducing the number of charge-trapping events.

In Fig. 5h, we compare the relative change in ΔVH over different measurement rounds on two double-gated GFETs, namely, D1 and D2 (Extended Data Fig. 7 and Supplementary Section 11). Throughout these ten measurements, there is an exponential dependence of ΔVH on the applied back-gate voltage, as expected from our theoretical calculations (Fig. 2b). An improvement of a factor of up to 4.5 is observed for a work-function shift of 340 meV, as shown in the corresponding comparison of the work function for these measurements (Fig. 5i). Thus, we observe an improvement of about 750 meV dec−1 when more negative back-gate voltages are applied, in good agreement with the theoretical results (Fig. 2b). However, in this double-gated configuration, full improvement cannot be achieved in every measurement round; in particular, for high VBG, a strong modulation of the work function by the back gate is hindered by the charging of oxide traps in SiO2. Nevertheless, all the hysteresis measurements show that a shift in the graphene work function to higher values, away from the defect band in Al2O3, successfully reduces the amount of electrically active border traps, thereby stabilizing the GFETs.

Stability under static gate bias

To evaluate the long-term stability of GFETs, we analysed the Dirac-voltage shifts (ΔVDirac) after static elevated gate voltages (VG,high) were applied for varying charging times (tcharging). We record the magnitude of the initial ΔVDirac shift and monitor the recovery after the increased gate-biasing period with fast ID (VG) sweeps at logarithmically spaced recovery times. In Fig. 6a, the fast ID (VG) sweeps recorded during the recovery from negative gate biasing (negative-bias temperature instability (NBTI)) at –10 V are shown. Such BTI measurements give a complementary perspective on the long-term stability and reliability of FETs compared with the hysteresis measurements discussed earlier. Although during hysteresis measurements, GFETs are subjected to slow up and down gate-voltage sweeps, during a BTI measurement, an elevated gate bias is applied for a certain charging time and the recovery of the Dirac point is recorded. With this well-established measurement scheme, the impact of border traps is studied12,13,15. Thus, in a BTI measurement, the observed hysteresis during the probing sweeps is small and not in focus. To avoid measurement artefacts coming from fast traps causing the hysteresis in the probing ID (VG) curves, the down-sweep ID (VG) curves are used to evaluate the VDirac shifts for all NBTI measurements and the up-sweep ID (VG) curves for positive gate biasing (positive-bias temperature instability (PBTI)) (Methods and Extended Data Fig. 8).

Fig. 6: Long-term electrical stability assessed using BTI.
figure 6

In BTI measurements, the FET is subjected to extended periods of elevated gate bias and the drifts in the Dirac voltage during the degradation and recovery periods are recorded. a, Type 1 device subjected to –10 V for 1 ks. b,c, Type 1 FET is subjected for increasing time spans to elevated NBTI gate bias of –10 V (b), resulting in a larger degradation than observed for the same conditions on Type 2 FETs (c). df, When applying an elevated PBTI voltage level of 10 V to the GFETs (d), the Dirac voltage of Type 1 devices drifts more and barely recovers (e) compared with their Type 2 counterparts (f). To avoid an impact of the measurement history, the measurements shown were performed on different devices.

NBTI measured by subjecting the devices to a gate bias of –10 V for increasingly long charging times is shown for Type 1 GFETs (Fig. 6b) and for Type 2 GFETs (Fig. 6c). As planned, the VDirac shifts are smaller on Type 2 devices than on Type 1 devices. GFETs based on Type 2 graphene are more stable with respect to long-term degradation because the graphene EF is further away from the Al2O3 defect band (Fig. 4d). Therefore, on Type 2 GFETs, fewer oxide traps change their charge state during negative gate biasing, resulting in smaller shifts in VDirac, which also recover faster as the traps that emit electrons are located closer to the interface and thus have smaller time constants. Extended Data Fig. 9 shows the recovery traces of NBTI at –5 V. For Type 2 GFETs, slight over-recovery13 is observed for the shortest charging time of 1 s (Supplementary Section 12). This over-recovery is also visible for the fast ID (VG) sweeps used for the BTI evaluation of Type 2 GFETs (Extended Data Fig. 10). In Fig. 6d, the fast ID (VG) sweeps measured after a positive bias at 10 V are shown, together with the corresponding recovery traces for Type 1 GFETs (Fig. 6e) and Type 2 GFETs (Fig. 6f). For both device types, degradation on applying positive biases (PBTI) are higher than NBTI shifts, as the Fermi level in graphene is at the lower edge of the Al2O3 defect band (Fig. 4c). Thus, the number of defects that become more negatively charged during a positive bias is larger than the number of defects that emit one of their electrons during a negative bias. As Type 2 graphene is more p doped, EF is located further away from the Al2O3 defect band, ultimately reducing the amount of charge trapping without the need to modify the insulator or reduce the total number of traps.

Interestingly, throughout the charging times, the shifts on Type 1 devices do not recover, whereas the shifts on Type 2 devices show complete recovery, even for a short charging time of 1 s. This observation was confirmed when subjecting the devices to a smaller gate-bias voltage of 5 V (Extended Data Fig. 9). We hypothesize the active creation of defects in Al2O3 to explain the permanent component of BTI degradation in Type 1 GFETs. In silicon FETs using SiO2 as a gate dielectric, the permanent component of BTI has been associated with gate-sided hydrogen release58. We speculate that a similar mechanism of bias-facilitated oxide defect creation in Al2O3 is responsible for the permanent PBTI observed for our GFETs, which will need to be investigated by future studies.


We have reported an approach to improve the electrical stability of FETs based on 2D materials. Charge trapping at the border traps in amorphous oxides is the principal cause of the threshold-voltage drifts and reduced long-term stability in 2D FETs. Therefore, the impact of defect bands in amorphous gate oxides can be reduced by tuning the energy alignment of the Fermi level. We demonstrate our approach using GFETs with Al2O3 as the top-gate oxide and two different types of graphene, which differ in their doping and Fermi-level alignments based on their respective fabrication methods. Our measurements show that the GFETs, which are based on the more p-doped Type 2 graphene with a higher (ET − EF), have a smaller hysteresis and increased stability of their Dirac voltage when subject to prolonged elevated gate biases. Furthermore, by electrostatic doping of the graphene channel via a back gate, the hysteresis width can be reduced by a factor of up to 4.5. These results suggest that more stable and reliable 2D-material-based FETs using common amorphous gate oxides can be built by minimizing the impact of defect bands in the gate oxides during design.

In 2D semiconductors, the design options mainly consist of choosing suitable materials depending on n or p doping, or varying the thickness of the channel material. There is more design freedom with graphene, as the graphene Fermi level can be tuned over a range of up to 2 eV. Moreover, our approach to improve stability may be universally applicable to other insulators, such as crystalline insulators, where the impact of narrow insulator-defect bands can be reduced further than in amorphous oxides8. However, future studies will be necessary to clarify what levels of stability can be achieved by Fermi-level tuning in systems based on amorphous oxides and crystalline insulators. In addition, the stability-based design approach relies on prior knowledge about the energy location of the defect bands in the oxide, which at the moment is incomplete.


Device fabrication

Our top-gated GFETs were fabricated on spin-coated PI substrates using photolithography. First, the flexible substrate was prepared by spin coating PI in the liquid form on a Si wafer and subsequently curing the layer. The thickness of the solidified PI film was about 8 μm. During the fabrication process, a rigid Si substrate was used as a support layer. In the next step, a CVD-grown graphene layer was transferred to the PI substrate. We study two batches of GFETs where the channel is formed by graphene samples purchased from different vendors, namely, vendor 1 (Type 1) and vendor 2 (Type 2). For Type 1 devices, CVD graphene was transferred from the copper growth substrate using a polymethyl methacrylate (PMMA)-assisted wet transfer method59; for Type 2 GFETs, the transfer was performed by vendor 2. The Type 1 graphene flake covered an area of 2 × 2 cm2 and was of higher quality than the Type 2 flake (which covered a six-inch wafer). The different qualities of the graphene layer were confirmed by Raman spectroscopy (Extended Data Fig. 3 and Supplementary Section 4). The graphene layer was patterned in an oxygen-plasma etch step to form channels with length (L) of 160 μm and width (W) of 100 μm. In the next step, the source and drain contacts were deposited by sputtering 50 nm Ni, followed by a lift-off process. This step was followed by growing 40 nm Al2O3 with atomic layer deposition (ALD) on top of the devices to form the gate oxide in a top-gated configuration. To finalize the GFETs, the top-gate electrode was fabricated by sputtering 10 nm Ti and 150 nm Al and patterned in a lift-off process. To be able to contact the source and drain pads, vias were opened through the Al2O3 with a wet-buffered oxide etchant. For our double-gated GFETs, we transferred CVD graphene monolayers from vendor 1 using a PMMA-assisted wet transfer method59 to a 90 nm SiO2 on a Si wafer. Subsequently, the graphene layer was patterned in an oxygen-plasma etch step to form channels of L = 80 μm and W = 50 μm and the source and drain contacts were deposited by sputtering 50 nm Ni followed by a lift-off process. After this step, the gate oxide of 40 nm Al2O3 was grown using ALD and the top-gate electrode was deposited with a sputter process.

Measurement technique

Our electrical measurements were performed in a vacuum at room temperature and in complete darkness. The devices were examined with the PI supported on a silicon wafer. From two-probe measurements, we extracted the field-effect mobility of the GFETs and found it to be 4,000 cm2 V−1 s−1 for Type 1 graphene and 1,000 cm2 V−1 s−1 for Type 2 graphene. The Hall mobility of both the samples was found to be slightly higher. The hysteresis was analysed by measuring the double-sweep IDVG characteristics using different sweep times tSW and sweep ranges of \({V}_{{{{\rm{Gmin}}}}}\) and \({V}_{{{{\rm{Gmax}}}}}\). The hysteresis width ΔVH was extracted as the difference between the forward- and reverse-sweep VDirac value. As suggested in our previous work20, we expressed the hysteresis dynamics using ΔVH (1/tSW) traces. Finally, the BTI degradation/recovery dynamics were analysed using subsequent degradation/recovery rounds with either fixed stress time tdeg and increasing high-voltage levels VG,high, or fixed VG,high and increasing tdeg. During the recovery period, we applied a constant recovery voltage of VG,recovery = 1 V between the sweeps. This voltage is chosen to be close to the charge-carrier equilibrium at VDirac. To avoid artefacts from fast traps charged during the sweep, the down-sweep IDVG curve is used to monitor the recovery of NBTI13. The characteristics obtained when using up-sweeps to measure the NBTI recovery are shown in Extended Data Fig. 8. For PBTI measurements, the recording of the up-sweep minimizes artefacts13; thus, we used IDVG sweeps from negative to positive voltages for the evaluation of PBTI. As was suggested in our previous study on GFETs13, we expressed the BTI degradation magnitude using a Dirac-point voltage shift ΔVDirac and plotted it versus the relaxation time tr. To gain more statistics, all our measurements were repeated on several devices.