Resonant-tunneling diodes are used in high-frequency oscillators, wave emitters and detectors, logic gates, photodetectors, and optoelectronic circuits.

Diodes are widely used electronic devices that act as one-way switches for current. A well-known example is an LED (light-emitting diode), but there is a special class of diodes designed to make use of the phenomenon known as “quantum tunneling." Called resonant-tunneling diodes (RTDs), they are among the fastest semiconductor devices and are used in countless practical applications, such as high-frequency oscillators in the terahertz band, wave emitters, wave detectors, and logic gates, to take only a few examples. RTDs are also sensitive to light and can be used as photodetectors or optically active elements in optoelectronic circuits.

Quantum tunneling (or the tunnel effect) is a phenomenon described by quantum mechanics in which particles are able to transition through a classically forbidden energy state. In other words, they can escape from a region surrounded by a potential barrier even if their kinetic energy is lower than the potential energy of the barrier. 

“RTDs consist of two potential barriers separated by a layer that forms a quantum well. This structure is sandwiched between extremities formed by semiconductor alloys with a high concentration of electrical charges, which are accelerated when a voltage is placed across the RTD. The tunnel effect occurs when the energy in the electrical charges accelerated by application of the voltage coincides with the quantized energy level in the quantum well. As the voltage is applied, the energy of the electrons retained by the barrier increases, and at a specific level, they are able to cross the forbidden region. However, if an even higher voltage is applied, the electrons can no longer get through because their energy exceeds the quantized energy in the well,” said Marcio Daldin Teodoro, a professor in the Physics Department of the Federal University of São Carlos (UFSCar), in the state of São Paulo, Brazil.

Teodoro was the principal investigator for a study that determined charge buildup and dynamics in RTDs throughout the applied voltage range. A paper describing the study is published in Physical Review Applied. The study was supported by FAPESP via four projects (13/18719-1, 14/19142-2, 14/02112-3, and 18/01914-0).

“The operation of RTD-based devices depends on several parameters, such as charge excitation, accumulation and transport, and the relationships among these properties,” Teodoro said. “Charge carrier density in these devices has always been determined before and after the resonance area, but not in the resonance area itself, which carries the key information. We used advanced spectroscopy and electronic transport techniques to determine charge accumulation and dynamics throughout the device. The tunneling signature is a peak current followed by a sharp drop to a specific voltage that depends on the RTD’s structural characteristics.”

Magnetic field

Previous studies measured charge carrier density as a function of voltage using the magneto-transport technique, which correlates current intensity and magnetic field. However, magneto-transport tools may not be able to characterize charge accumulation throughout the operating range, and there can be blind spots for certain voltage values. As a result, the researchers also used a technique called magneto-electroluminescence, which investigates the light emission induced by the voltage applied as a function of the magnetic field.

“Magneto-electroluminescence enabled us to study voltage bands that were magneto-transport blind spots. The results matched at points where charge density can be measured by both techniques,” said Edson Rafael Cardozo de Oliveira, first author of the paper. “These two experimental techniques proved complementary for a complete investigation of charge density across the entire RTD operating voltage range.”

Cardozo de Oliveira earned a Ph.D. in physics with Teodoro as his thesis advisor, after a sandwich doctorate in Germany at the University of Würzburg’s Department of Technical Physics. Among his other contributions to the study was writing the software used to process the huge amount of data, on the order of gigabytes, produced by the experiments.

“The study can guide further research on RTDs, potentially leading to the production of more efficient optoelectronic devices,” he said. “By monitoring charge buildup as a function of voltage, it will be possible to develop novel RTDs with optimized charge distribution to enhance photodetection efficiency or minimize optical losses.”

Because RTDs are such complex structures, knowing how charges are distributed in them is important. “We now have a more complete map of RTD charge distribution,” said Victor Lopez Richard, a professor at UFSCar and a co-author of the paper.

The paper “Determination of carrier density and dynamics via magneto-electroluminescence spectroscopy in resonant-tunneling diodes” is at: journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.15.014042.

A new study led by researchers at Johns Hopkins and the University of Pennsylvania uses supercomputer modeling to suggest that eviction bans authorized during the COVID-19 pandemic reduced the infection rate and not only protected those who would have lost their housing but also entire communities from the spread of infections.

With widespread job loss in the U.S. during the pandemic, many state and local governments temporarily halted evictions last spring, and just as these protections were about to expire in September, the Centers for Disease Control and Prevention (CDC) declared a national eviction ban.

However, the order is only extended a few months at a time and is under constant challenge in the court system, including debates about whether such measures control infection transmission. 

The research team aimed to study if eviction bans help control the spread of SARS-CoV-2, the virus that causes COVID-19, explains Alison Hill, Ph.D., an assistant professor of biomedical engineering at Johns Hopkins.

In a bid to document the potential impact, Hill and Michael Levy, Ph.D., of the University of Pennsylvania, teamed up with experts in housing policy from the University of Illinois Urbana-Champaign. Hill and Levy specialize in using mathematical models to study how infections spread.

In the new report, the investigators say they used simulations to predict the number of additional SARS-CoV-2 infections in major U.S. cities if evictions had been allowed to occur during the fall of last year.

They estimated, for example, that in a city of approximately 1 million residents with evictions occurring at a heightened rate of 1% of households per month, an additional 4% of the population could become infected with, which corresponds to about 40,000 more cases. Even with a much lower eviction rate of 0.25% per month, which is similar to the pre-pandemic level in cities such as Atlanta, Detroit, and Tucson, Arizona, estimates were for about 5,000 additional cases.

To make these predictions, the researchers first calibrated their math model to re-create the most common epidemic patterns seen in major U.S. cities in 2020. The model took into account changes in infection rates over time due to public health measures, and it was tailored to match reported COVID-19 cases and deaths. The researchers used the model to track the spread of infection in and out of households. Then, they ran another version of the model in which eviction bans were lifted, to estimate how the bans have affected the transmission of the virus.

Hill and her colleagues found that without eviction bans, people who are evicted or who live in a household that hosts evictees have 1.5 to 2.5 times more risk of being infected with than if the eviction bans were in place.

“People who experience eviction often move in with other households, increasing the density of people living together,” says Hill. “Households are known to be an important setting for SARS-CoV-2 infection, so this can increase transmission rates.”

The researchers’ supercomputer simulations also found that without eviction bans, the risk of SARS-CoV-2 infection would rise for all residents of a city, not just those who are evicted.

Even when the researchers evaluated a different version of the model, in which a city is divided into neighborhoods of different socioeconomic status and evictions are restricted to certain districts, evictions still could cause increases in infections with the virus.

“Some opponents of eviction bans say that evictions only affect a narrow part of the population, but our simulations indicate that evictions not only put disadvantaged households at risk of infection but entire communities as well,” says Hill. “When it comes to a transmissible disease like COVID-19, no neighborhood is entirely isolated.”

When the researchers used this data to examine how evictions would specifically impact Philadelphia residents, they found that people in all neighborhoods of the city would experience increased COVID-19 levels due to evictions.

Teaming up with researchers from Northeastern University who used de-identified information about how city residents travel throughout neighborhoods, the researchers estimated that, without eviction bans, there could have been approximately 5,000 more COVID-19 cases in Philadelphia if evictions occurred at pre-pandemic levels, and up to 50,000 additional cases if evictions were five times more frequent.

An early version of this research was cited in court cases challenging eviction bans in Philadelphia, and in the CDC’s national eviction order (click here and here).

To alleviate infection risk and reduce economic burdens, the researchers say, governments should consider not only extended eviction bans but also financial assistance for both tenants and landlords, as well as resources for households to reduce transmission of the virus within the home.

In addition to Hill and Levy, researchers who contributed to the study include co-first authors Anjalika Nande from Harvard University and Justin Sheen from Princeton University; Andrei Gheorghe and Ben Adlam at Harvard University; Julianna Shinnick and Maria Florencia Tejeda at the University of Pennsylvania; Emma Walters, Andrew Greenlee and Daniel Schneider at the University of Illinois Urbana-Champaign; and Brennan Klein, Matteo Chinazzi, Samuel Scarpino and Alessandro Vespignani at Northeastern University.