Lead halide materials have seen a recent surge of interest from the photovoltaics community following the observation of surprisingly high photovoltaic performance, with optoelectronic properties similar to GaAs. This begs the question: What is the limit for the efficiency of these materials? It has been known that under 1-sun illumination the efficiency limit of crystalline silicon is ∼29%, despite the Shockley-Queisser (SQ) limit for its bandgap being ∼33%: the discrepancy is due to strong Auger recombination. In this article, we show that methyl ammonium lead iodide (MAPbI) likewise has a larger than expected Auger coefficient. Auger nonradiative recombination decreases the theoretical external luminescence efficiency to ∼95% at open-circuit conditions. The Auger penalty is much reduced at the operating point where the carrier density is less, producing an oddly high fill factor of ∼90.4%. This compensates the Auger penalty and leads to a power conversion efficiency of 30.5%, close to ideal for the MAPbI bandgap.
Thermophotovoltaic power conversion utilizes thermal radiation from a local heat source to generate electricity in a photovoltaic cell. It was shown in recent years that the addition of a highly reflective rear mirror to a solar cell maximizes the extraction of luminescence. This, in turn, boosts the voltage, enabling the creation of record-breaking solar efficiency. Now we report that the rear mirror can be used to create thermophotovoltaic systems with unprecedented high thermophotovoltaic efficiency. This mirror reflects low-energy infrared photons back into the heat source, recovering their energy. Therefore, the rear mirror serves a dual function; boosting the voltage and reusing infrared thermal photons. This allows the possibility of a practical >50% efficient thermophotovoltaic system. Based on this reflective rear mirror concept, we report a thermophotovoltaic efficiency of 29.1 ± 0.4% at an emitter temperature of 1,207 °C.
Analog hardware accelerators, which perform computation within a dense memory array, have the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. Exploiting the intrinsic computational advantages of memory arrays, however, has proven to be challenging principally due to the overhead imposed by the peripheral circuitry and due to the non-ideal properties of memory devices that play the role of the synapse. We review the existing implementations of these accelerators for deep supervised learning, organizing our discussion around the different levels of the accelerator design hierarchy, with an emphasis on circuits and architecture. We explore and consolidate the various approaches that have been proposed to address the critical challenges faced by analog accelerators, for both neural network inference and training, and highlight the key design trade-offs underlying these techniques.
The greatest source of loss in conventional single-junction photovoltaic cells is their inefficient utilization of the energy contained in the full spectrum of sunlight. To overcome this deficiency, we propose a multijunction system that laterally splits the solar spectrum onto a planar array of single-junction cells with different band gaps. As a first demonstration, we designed, fabricated, and characterized dispersive diffractive optics that spatially separated the visible (360–760 nm) and near-infrared (760–1100 nm) bands of sunlight in the far field. Inverse electromagnetic design was used to optimize the surface texture of the thin diffractive phase element. An optimized thin film fabricated by femtosecond two-photon absorption 3D direct laser writing shows an average splitting ratio of 69.5% between the visible and near-infrared light over the 380–970 nm range at normal incidence. The splitting efficiency is predicted to be 80.4% assuming a structure without fabrication errors. Spectral-splitting action is observed within an angular range of ±1° from normal incidence. Further design optimization and fabrication improvements can increase the splitting efficiency under direct sunlight, increase the tolerance to angular errors, allow for a more compact geometry, and ultimately incorporate a greater number of photovoltaic band gaps.
In neuromorphic computing, artificial synapses provide a multi‐weight (MW) conductance state that is set based on inputs from neurons, analogous to the brain. Herein, artificial synapses based on magnetic materials that use a magnetic tunnel junction (MTJ) and a magnetic domain wall (DW) are explored. By fabricating lithographic notches in a DW track underneath a single MTJ, 3–5 stable resistance states that can be repeatably controlled electrically using spin‐orbit torque are achieved. The effect of geometry on the synapse behavior is explored, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a rectangular device has higher stochasticity, but with stable resistance levels. The device data is input into neuromorphic computing simulators to show the usefulness of application‐specific synaptic functions. Implementing an artificial neural network (NN) applied to streamed Fashion‐MNIST data, the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional NN for CIFAR‐100 image recognition, the rectangular magnetic synapse achieves near‐ideal inference accuracy, due to the stability of its resistance levels. This work shows MW magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.
Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip–flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.
CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.
We consider a near-field electroluminescent refrigeration device. The device uses a GaAs light emitting diode as the cold side, and a Si photovoltaic cell as the hot side. The two sides are brought in close proximity to each other across a vacuum gap. The cooling is achieved by applying a positive bias on the GaAs light emitting diode. We show that the choice of GaAs and Si here can suppress the non-idealities for electroluminescent cooling purposes: GaAs has a wide bandgap with low Auger recombination, and Si is a non-polar semiconductor which leads to significantly reduced sub-bandgap heat transfer. We show that by using this configuration in the near-field regime, the cooling power density can reach 10 5 W=m 2 even in the presence of realistic Auger recombination and Shockley-Read-Hall recombination. In addition, with photovoltaic power recovery from the Si cell, the efficiency of the device can be further improved. Our work points to the significant potential of combining near-field heat transfer with active semiconductor devices for the control of heat flow.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.