Abstract Designing a new heterostructure electrode has many challenges associated with interface engineering. Demanding simulation resources and lack of heterostructure databases continue to be a barrier to understanding the chemistry and mechanics of complex interfaces using simulations. Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally investigates the interface between 2D graphene and 3D tin (Sn) systems with density functional theory (DFT) method. This computationally demanding simulation data is further used to develop machine learning (ML)-based potential energy surfaces (PES). The approach to developing PES for complex interface systems in the light of limited data and the transferability of such models has been discussed. To develop PES for graphene-tin interface systems, high-dimensional neural networks (HDNN) are used that rely on atom-centered symmetry function to represent structural information. HDNN are modified to train on the total energies of the interface system rather than atomic energies. The performance of modified HDNN trained on 5789 interface structures of graphene|Sn is tested on new interfaces of the same material pair with varying levels of structural deviations from the training dataset. Root-mean-squared error (RMSE) for test interfaces fall in the range of 0.01–0.45 eV/atom, depending on the structural deviations from the reference training dataset. By avoiding incorrect decomposition of total energy into atomic energies, modified HDNN model is shown to obtain higher accuracy and transferability despite a limited dataset. Improved accuracy in the ML-based modeling approach promises cost-effective means of designing interfaces in heterostructure energy storage systems with higher cycle life and stability.
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Electronic Transport and Thermopower in 2D and 3D Heterostructures—A Theory Perspective
The impact of interfaces and heterojuctions on the electronic and thermoelectric transport properties of materials is discussed herein. Recent progress in understanding electronic transport in heterostructures of 2D materials ranging from graphene to transition metal dichalcogenides, their homojunctions (grain boundaries), lateral heterojunctions (such as graphene/MoS2lateral interfaces), and vertical van der Waals heterostructures is reviewed. Work on thermopower in 2D heterojunctions, as well as their applications in creating devices such as resonant tunneling diodes (RTDs), is also discussed. Last, the focus turns to work in 3D heterostructures. While transport in 3D heterostructures has been researched for several decades, here recent progress in theory and simulation of quantum effects on transport via the Wigner and non‐equilibrium Green's functions approaches is reviewed. These simulation techniques have been successfully applied toward understanding the impact of heterojunctions on transport properties and thermopower, which finds applications in energy harvesting, and electron resonant tunneling, with applications in RTDs. In conclusion, tremendous progress has been made in both simulation and experiments toward the goal of understanding transport in heterostructures and this progress will soon be parlayed into improved energy converters and quantum nanoelectronic devices.
more » « less- NSF-PAR ID:
- 10460498
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Annalen der Physik
- Volume:
- 531
- Issue:
- 9
- ISSN:
- 0003-3804
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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A 15-μm-diam-mesa device was defined by standard planar processing including a top annular ohmic contact with a 5-μm-diam pinhole in the center to couple out enough of the internal emission for accurate free-space power measurements [4]. The emission spectra have the behavior displayed in Fig. 1(b), parameterized by bias voltage (VB). The long wavelength emission edge is at = 1684 nm - close to the In0.53Ga0.47As bandgap energy of Ug ≈ 0.75 eV at 300 K. The spectral peaks for VB = 2.8 and 3.0 V both occur around = 1550 nm (h = 0.75 eV), so blue-shifted relative to the peak of the “ideal”, bulk InGaAs emission spectrum shown in Fig. 1(b) [5]. These results are consistent with the model displayed in Fig. 1(c), whereby the broad emission peak is attributed to the radiative recombination between electrons accumulated on the emitter side, and holes generated on the emitter side by interband tunneling with current density Jinter. The blue-shifted main peak is attributed to the quantum-size effect on the emitter side, which creates a radiative recombination rate RN,2 comparable to the band-edge cross-gap rate RN,1. Further support for this model is provided by the shorter wavelength and weaker emission peak shown in Fig. 1(b) around = 1148 nm. Our quantum mechanical calculations attribute this to radiative recombination RR,3 in the RTD quantum well between the electron ground-state level E1,e, and the hole level E1,h. To further test the model and estimate quantum efficiencies, we conducted optical power measurements using a large-area Ge photodiode located ≈3 mm away from the RTD pinhole, and having spectral response between 800 and 1800 nm with a peak responsivity of ≈0.85 A/W at =1550 nm. Simultaneous I-V and L-V plots were obtained and are plotted in Fig. 2(a) with positive bias on the top contact (emitter on the bottom). The I-V curve displays a pronounced NDR region having a current peak-to-valley current ratio of 10.7 (typical for In0.53Ga0.47As RTDs). The external quantum efficiency (EQE) was calculated from EQE = e∙IP/(∙IE∙h) where IP is the photodiode dc current and IE the RTD current. The plot of EQE is shown in Fig. 2(b) where we see a very rapid rise with VB, but a maximum value (at VB= 3.0 V) of only ≈2×10-5. To extract the internal quantum efficiency (IQE), we use the expression EQE= c ∙i ∙r ≡ c∙IQE where ci, and r are the optical-coupling, electrical-injection, and radiative recombination efficiencies, respectively [6]. Our separate optical calculations yield c≈3.4×10-4 (limited primarily by the small pinhole) from which we obtain the curve of IQE plotted in Fig. 2(b) (right-hand scale). The maximum value of IQE (again at VB = 3.0 V) is 6.0%. From the implicit definition of IQE in terms of i and r given above, and the fact that the recombination efficiency in In0.53Ga0.47As is likely limited by Auger scattering, this result for IQE suggests that i might be significantly high. To estimate i, we have used the experimental total current of Fig. 2(a), the Kane two-band model of interband tunneling [7] computed in conjunction with a solution to Poisson’s equation across the entire structure, and a rate-equation model of Auger recombination on the emitter side [6] assuming a free-electron density of 2×1018 cm3. We focus on the high-bias regime above VB = 2.5 V of Fig. 2(a) where most of the interband tunneling should occur in the depletion region on the collector side [Jinter,2 in Fig. 1(c)]. 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Fig. 3(b) shows the tunneling probability T according to the Kane two-band model in the three materials, In0.53Ga0.47As, GaAs, and GaN, following our observation of a similar electroluminescence mechanism in GaN/AlN RTDs (due to strong polarization field of wurtzite structures) [8]. The expression is Tinter = (2/9)∙exp[(-2 ∙Ug 2 ∙me)/(2h∙P∙E)], where Ug is the bandgap energy, P is the valence-to-conduction-band momentum matrix element, and E is the electric field. Values for the highest calculated internal E fields for the InGaAs and GaN are also shown, indicating that Tinter in those structures approaches values of ~10-5. As shown, a GaAs RTD would require an internal field of ~6×105 V/cm, which is rarely realized in standard GaAs RTDs, perhaps explaining why there have been few if any reports of room-temperature electroluminescence in the GaAs devices. [1] E.R. Brown,et al., Appl. Phys. Lett., vol. 58, 2291, 1991. [5] S. Sze, Physics of Semiconductor Devices, 2nd Ed. 12.2.1 (Wiley, 1981). [2] M. Feiginov et al., Appl. Phys. Lett., 99, 233506, 2011. [6] L. Coldren, Diode Lasers and Photonic Integrated Circuits, (Wiley, 1995). [3] Y. Nishida et al., Nature Sci. Reports, 9, 18125, 2019. [7] E.O. Kane, J. of Appl. Phy 32, 83 (1961). [4] P. Fakhimi, et al., 2019 DRC Conference Digest. [8] T. Growden, et al., Nature Light: Science & Applications 7, 17150 (2018). [5] S. Sze, Physics of Semiconductor Devices, 2nd Ed. 12.2.1 (Wiley, 1981). [6] L. Coldren, Diode Lasers and Photonic Integrated Circuits, (Wiley, 1995). [7] E.O. Kane, J. of Appl. Phy 32, 83 (1961). [8] T. Growden, et al., Nature Light: Science & Applications 7, 17150 (2018).more » « less
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