Resonant tunneling diodes (RTDs) have come full-circle in the past 10 years after their demonstration in the early 1990s
as the fastest room-temperature semiconductor oscillator, displaying experimental results up to 712 GHz and fmax values
exceeding 1.0 THz [1]. Now the RTD is once again the preeminent electronic oscillator above 1.0 THz and is being
implemented as a coherent source [2] and a self-oscillating mixer [3], amongst other applications. This paper concerns
RTD electroluminescence – an effect that has been studied very little in the past 30+ years of RTD development, and not
at room temperature. We present experiments and modeling of an n-type In0.53Ga0.47As/AlAs double-barrier RTD
operating as a cross-gap light emitter at ~300K. The MBE-growth stack is shown in Fig. 1(a). 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)]. And because of the high-quality of the InGaAs/AlAs heterostructure (very few
traps or deep levels), most of the holes should reach the emitter side by some combination of drift, diffusion, and tunneling
through the valence-band double barriers (Type-I offset) between InGaAs and AlAs. The computed interband current
density Jinter is shown in Fig. 3(a) along with the total current density Jtot. At the maximum Jinter (at VB=3.0 V) of 7.4×102
A/cm2, we get i = Jinter/Jtot = 0.18, which is surprisingly high considering there is no p-type doping in the device. When
combined with the Auger-limited r of 0.41 and c ≈ 3.4×10-4, we find a model value of IQE = 7.4% in good agreement
with experiment. This leads to the model values for EQE plotted in Fig. 2(b) - also in good agreement with experiment.
Finally, we address the high Jinter and consider a possible universal nature of the light-emission mechanism. 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
Physics-informed reduced-order learning from the first principles for simulation of quantum nanostructures
Multi-dimensional direct numerical simulation (DNS) of the Schrödinger equation is needed for design and analysis of quantum nanostructures that offer numerous applications in biology, medicine, materials, electronic/photonic devices, etc. In large-scale nanostructures, extensive computational effort needed in DNS may become prohibitive due to the high degrees of freedom (DoF). This study employs a physics-based reduced-order learning algorithm, enabled by the first principles, for simulation of the Schrödinger equation to achieve high accuracy and efficiency. The proposed simulation methodology is applied to investigate two quantum-dot structures; one operates under external electric field, and the other is influenced by internal potential variation with periodic boundary conditions. The former is similar to typical operations of nanoelectronic devices, and the latter is of interest to simulation and design of nanostructures and materials, such as applications of density functional theory. In each structure, cases within and beyond training conditions are examined. Using the proposed methodology, a very accurate prediction can be realized with a reduction in the DoF by more than 3 orders of magnitude and in the computational time by 2 orders, compared to DNS. An accurate prediction beyond the training conditions, including higher external field and larger internal potential in untrained quantum states, is also achieved. Comparison is also carried out between the physics-based learning and Fourier-based plane-wave approaches for a periodic case.
more » « less- Award ID(s):
- 1852102
- NSF-PAR ID:
- 10407063
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
INTRODUCTION Solving quantum many-body problems, such as finding ground states of quantum systems, has far-reaching consequences for physics, materials science, and chemistry. Classical computers have facilitated many profound advances in science and technology, but they often struggle to solve such problems. Scalable, fault-tolerant quantum computers will be able to solve a broad array of quantum problems but are unlikely to be available for years to come. Meanwhile, how can we best exploit our powerful classical computers to advance our understanding of complex quantum systems? Recently, classical machine learning (ML) techniques have been adapted to investigate problems in quantum many-body physics. So far, these approaches are mostly heuristic, reflecting the general paucity of rigorous theory in ML. Although they have been shown to be effective in some intermediate-size experiments, these methods are generally not backed by convincing theoretical arguments to ensure good performance. RATIONALE A central question is whether classical ML algorithms can provably outperform non-ML algorithms in challenging quantum many-body problems. We provide a concrete answer by devising and analyzing classical ML algorithms for predicting the properties of ground states of quantum systems. We prove that these ML algorithms can efficiently and accurately predict ground-state properties of gapped local Hamiltonians, after learning from data obtained by measuring other ground states in the same quantum phase of matter. Furthermore, under a widely accepted complexity-theoretic conjecture, we prove that no efficient classical algorithm that does not learn from data can achieve the same prediction guarantee. By generalizing from experimental data, ML algorithms can solve quantum many-body problems that could not be solved efficiently without access to experimental data. RESULTS We consider a family of gapped local quantum Hamiltonians, where the Hamiltonian H ( x ) depends smoothly on m parameters (denoted by x ). The ML algorithm learns from a set of training data consisting of sampled values of x , each accompanied by a classical representation of the ground state of H ( x ). These training data could be obtained from either classical simulations or quantum experiments. During the prediction phase, the ML algorithm predicts a classical representation of ground states for Hamiltonians different from those in the training data; ground-state properties can then be estimated using the predicted classical representation. Specifically, our classical ML algorithm predicts expectation values of products of local observables in the ground state, with a small error when averaged over the value of x . The run time of the algorithm and the amount of training data required both scale polynomially in m and linearly in the size of the quantum system. Our proof of this result builds on recent developments in quantum information theory, computational learning theory, and condensed matter theory. Furthermore, under the widely accepted conjecture that nondeterministic polynomial-time (NP)–complete problems cannot be solved in randomized polynomial time, we prove that no polynomial-time classical algorithm that does not learn from data can match the prediction performance achieved by the ML algorithm. In a related contribution using similar proof techniques, we show that classical ML algorithms can efficiently learn how to classify quantum phases of matter. In this scenario, the training data consist of classical representations of quantum states, where each state carries a label indicating whether it belongs to phase A or phase B . The ML algorithm then predicts the phase label for quantum states that were not encountered during training. The classical ML algorithm not only classifies phases accurately, but also constructs an explicit classifying function. Numerical experiments verify that our proposed ML algorithms work well in a variety of scenarios, including Rydberg atom systems, two-dimensional random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases. CONCLUSION We have rigorously established that classical ML algorithms, informed by data collected in physical experiments, can effectively address some quantum many-body problems. These rigorous results boost our hopes that classical ML trained on experimental data can solve practical problems in chemistry and materials science that would be too hard to solve using classical processing alone. Our arguments build on the concept of a succinct classical representation of quantum states derived from randomized Pauli measurements. Although some quantum devices lack the local control needed to perform such measurements, we expect that other classical representations could be exploited by classical ML with similarly powerful results. How can we make use of accessible measurement data to predict properties reliably? Answering such questions will expand the reach of near-term quantum platforms. Classical algorithms for quantum many-body problems. Classical ML algorithms learn from training data, obtained from either classical simulations or quantum experiments. Then, the ML algorithm produces a classical representation for the ground state of a physical system that was not encountered during training. Classical algorithms that do not learn from data may require substantially longer computation time to achieve the same task.more » « less
-
BACKGROUND The past decade has witnessed considerable progress toward the creation of new quantum technologies. Substantial advances in present leading qubit technologies, which are based on superconductors, semiconductors, trapped ions, or neutral atoms, will undoubtedly be made in the years ahead. Beyond these present technologies, there exist blueprints for topological qubits, which leverage fundamentally different physics for improved qubit performance. These qubits exploit the fact that quasiparticles of topological quantum states allow quantum information to be encoded and processed in a nonlocal manner, providing inherent protection against decoherence and potentially overcoming a major challenge of the present generation of qubits. Although still far from being experimentally realized, the potential benefits of this approach are evident. The inherent protection against decoherence implies better scalability, promising a considerable reduction in the number of qubits needed for error correction. Transcending possible technological applications, the underlying physics is rife with exciting concepts and challenges, including topological superconductors, non-abelian anyons such as Majorana zero modes (MZMs), and non-abelian quantum statistics. ADVANCES In a wide-ranging and ongoing effort, numerous potential material platforms are being explored that may realize the required topological quantum states. Non-abelian anyons were first predicted as quasiparticles of topological states known as fractional quantum Hall states, which are formed when electrons move in a plane subject to a strong perpendicular magnetic field. The prediction that hybrid materials that combine topological insulators and conventional superconductors can support localized MZMs, the simplest type of non-abelian anyon, brought entirely new material platforms into view. These include, among others, semiconductor-superconductor hybrids, magnetic adatoms on superconducting substrates, and Fe-based superconductors. One-dimensional systems are playing a particularly prominent role, with blueprints for quantum information applications being most developed for hybrid semiconductor-superconductor systems. There have been numerous attempts to observe non-abelian anyons in the laboratory. Several experimental efforts observed signatures that are consistent with some of the theoretical predictions for MZMs. A few extensively studied platforms were subjected to intense scrutiny and in-depth analyses of alternative interpretations, revealing a more complex reality than anticipated, with multiple possible interpretations of the data. Because advances in our understanding of a physical system often rely on discrepancies between experiment and theory, this has already led to an improved understanding of Majorana signatures; however, our ability to detect and manipulate non-abelian anyons such as MZMs remains in its infancy. Future work can build on improved materials in some of the existing platforms but may also exploit new materials such as van der Waals heterostructures, including twisted layers, which promise many new options for engineering topological phases of matter. OUTLOOK Experimentally establishing the existence of non-abelian anyons constitutes an outstandingly worthwhile goal, not only from the point of view of fundamental physics but also because of their potential applications. Future progress will be accelerated if claims of Majorana discoveries are based on experimental tests that go substantially beyond indicators such as zero-bias peaks that, at best, suggest consistency with a Majorana interpretation. It will be equally important that these discoveries build on an excellent understanding of the underlying material systems. Most likely, further material improvements of existing platforms and the exploration of new material platforms will both be important avenues for progress toward obtaining solid evidence for MZMs. Once that has been achieved, we can hope to explore—and harness—the fascinating physics of non-abelian anyons such as the topologically protected ground state manifold and non-abelian statistics. Proposed topological platforms. (Left) Proposed state of electrons in a high magnetic field (even-denominator fractional quantum Hall states) are predicted to host Majorana quasiparticles. (Right) Hybrid structures of superconductors and other materials have also been proposed to host such quasiparticles and can be tailored to create topological quantum bits based on Majoranas.more » « less
-
Designing Programmable Ferromagnetic Soft Metastructures for Minimally Invasive Endovascular TherapyMinimally invasive endovascular therapy (MIET) is an innovative technique that utilizes percutaneous access and transcatheter implantation of medical devices to treat vascular diseases. However, conventional devices often face limitations such as incomplete or suboptimal treatment, leading to issues like recanalization in brain aneurysms, endoleaks in aortic aneurysms, and paravalvular leaks in cardiac valves. In this study, we introduce a new metastructure design for MIET employing re-entrant honeycomb structures with negative Poisson's ratio (NPR), which are initially designed through topology optimization and subsequently mapped onto a cylindrical surface. Using ferromagnetic soft materials, we developed structures with adjustable mechanical properties called magnetically activated structures (MAS). These magnetically activated structures can change shape under noninvasive magnetic fields, letting them fit against blood vessel walls to fix leaks or movement issues. The soft ferromagnetic materials allow the stent design to be remotely controlled, changed, and rearranged using external magnetic fields. This offers accurate control over stent placement and positioning inside blood vessels. We performed magneto-mechanical simulations to evaluate the proposed design's performance. Experimental tests were conducted on prototype beams to assess their bending and torsional responses to external magnetic fields. The simulation results were compared with experimental data to determine the accuracy of the magneto-mechanical simulation model for ferromagnetic soft materials. After validating the model, it was used to analyze the deformation behavior of the plane matrix and cylindrical structure designs of the Negative Poisson's Ratio (NPR) metamaterial. The results indicate that the plane matrix NPR metamaterial design exhibits concurrent vertical and horizontal expansion when subjected to an external magnetic field. In contrast, the cylindrical structure demonstrates simultaneous axial and radial expansion under the same conditions. The preliminary findings demonstrate the considerable potential and practicality of the proposed methodology in the development of magnetically activated MIET devices, which offer biocompatibility, a diminished risk of adverse reactions, and enhanced therapeutic outcomes. Integrating ferromagnetic soft materials into mechanical metastructures unlocks promising opportunities for designing stents with adjustable mechanical properties, propelling the field towards more sophisticated minimally invasive vascular interventions.more » « less
-
more » « less
Planar photonics technology is expected to facilitate new physics and enhanced functionality for a new generation of disruptive optical devices. To analyze such planar optical metasurfaces efficiently, we propose a prismatic discontinuous Galerkin time domain (DGTD) method with a generalized dispersive material (GDM) model to conduct the full-wave electromagnetic simulation of planar photonic nanostructures. Prism-based DGTD allows for triangular prismatic space discretization, which is optimal for planar geometries. In order to achieve an accurate universal model for arbitrary dispersive materials, the GDM model is integrated within the prism-based DGTD. As an advantage of prismatic spatial discretization, the prism-based DGTD with GDM has fewer elements than conventional tetrahedral methods, which in turn brings higher computational efficiency. Finally, the accuracy, convergence behavior, and efficiency improvements of the proposed algorithm is validated by several numerical examples. A simulation toolkit with the proposed algorithm has been released online, enabling users to efficiently analyze metasurfaces with customized pixel patterns.
