Tag Archives: letter-mems-tech

A new way of arranging advanced computer components called memristors on a chip could enable them to be used for general computing, which could cut energy consumption by a factor of 100.

This would improve performance in low power environments such as smartphones or make for more efficient supercomputers, says a University of Michigan researcher.

This is the memristor array situated on a circuit board. Credit: Mohammed Zidan, Nanoelectronics group, University of Michigan.

“Historically, the semiconductor industry has improved performance by making devices faster. But although the processors and memories are very fast, they can’t be efficient because they have to wait for data to come in and out,” said Wei Lu, U-M professor of electrical and computer engineering and co-founder of memristor startup Crossbar Inc.

Memristors might be the answer. Named as a portmanteau of memory and resistor, they can be programmed to have different resistance states–meaning they store information as resistance levels. These circuit elements enable memory and processing in the same device, cutting out the data transfer bottleneck experienced by conventional computers in which the memory is separate from the processor.

However, unlike ordinary bits, which are 1 or 0, memristors can have resistances that are on a continuum. Some applications, such as computing that mimics the brain (neuromorphic), take advantage of the analog nature of memristors. But for ordinary computing, trying to differentiate among small variations in the current passing through a memristor device is not precise enough for numerical calculations.

Lu and his colleagues got around this problem by digitizing the current outputs–defining current ranges as specific bit values (i.e., 0 or 1). The team was also able to map large mathematical problems into smaller blocks within the array, improving the efficiency and flexibility of the system.

Computers with these new blocks, which the researchers call “memory-processing units,” could be particularly useful for implementing machine learning and artificial intelligence algorithms. They are also well suited to tasks that are based on matrix operations, such as simulations used for weather prediction. The simplest mathematical matrices, akin to tables with rows and columns of numbers, can map directly onto the grid of memristors.

Once the memristors are set to represent the numbers, operations that multiply and sum the rows and columns can be taken care of simultaneously, with a set of voltage pulses along the rows. The current measured at the end of each column contains the answers. A typical processor, in contrast, would have to read the value from each cell of the matrix, perform multiplication, and then sum up each column in series.

“We get the multiplication and addition in one step. It’s taken care of through physical laws. We don’t need to manually multiply and sum in a processor,” Lu said.

His team chose to solve partial differential equations as a test for a 32×32 memristor array–which Lu imagines as just one block of a future system. These equations, including those behind weather forecasting, underpin many problems science and engineering but are very challenging to solve. The difficulty comes from the complicated forms and multiple variables needed to model physical phenomena.

When solving partial differential equations exactly is impossible, solving them approximately can require supercomputers. These problems often involve very large matrices of data, so the memory-processor communication bottleneck is neatly solved with a memristor array. The equations Lu’s team used in their demonstration simulated a plasma reactor, such as those used for integrated circuit fabrication.

Imec, a research and innovation hub in nanoelectronics and digital technologies, announces that Niels Verellen, one of its young scientists, has been awarded an ERC Starting Grant. The grant of 1.5 million euros (for 5 years) will be used to enable high-resolution, fast, robust, zero-maintenance, inexpensive and ultra-compact microscopy technology based on on-chip photonics and CMOS image sensors. The technology paves the way for multiple applications of cell imaging in life sciences, biology, and medicine and compact, cost-effective DNA sequencing instruments.

Microscopy is an indispensable tool in biology and medicine that has fueled many breakthroughs. Recently the world of microscopy has witnessed a true revolution in terms of increased resolution of fluorescent imaging techniques, including a Nobel Prize in 2014. Yet, these techniques remain largely locked-up in specialized laboratories as they require bulky, expensive instrumentation and highly skilled operators.

The next big push in microscopy with a large societal impact will come from extremely compact and robust optical systems that will make high-resolution microscopy highly accessible and as such facilitate the diagnosis and treatment of diseases or disorders caused by problems at the cell or molecular level, such as meningitis, malaria, diabetes, cancer, and Alzheimer’s disease. Moreover, it will pave the way to DNA analysis as a more standard procedure, not only for the diagnosis of genomic disorders or in forensics, but also in cancer treatment, follow-up of transplants, the microbiome, pre-natal tests, and even agriculture, and archeology.

Niels Verellen, Senior Photonics Researcher & project leader at imec: “Compact, high-resolution and high-throughput microscopy devices will induce a profound change in the way cell biologists do research, in the way DNA sequencing becomes more and more accessible, in the way certain diseases can be diagnosed, new drugs are screened in the pharma industry, and healthcare workers can diagnose patients in remote areas.”

The topic of Verellen’s ERC grant is the development of Integrated high-Resolution On-Chip Structured Illumination Microscopy (IROCSIM). This new technology is based on a novel imaging platform that integrates active on-chip photonics and CMOS image sensors. “Whereas existing microscopy techniques today suffer from a trade-off between equipment size, field-of-view, and resolution, the IROCSIM solution will eliminate the need for bulky optical components and enable microscopy in the smallest possible form-factor, with a scalable field-of-view and without compromising the resolution,” continues Verellen.

The European Research Council (ERC) is a pan European funding body designed to support investigator-driven frontier research and stimulate scientific excellence across Europe. The ERC aims to support the best and most creative scientists to identify and explore new opportunities and directions in any field of research. ERC Starting grants in particular are designed to support outstanding researchers with 2 to 7 years postdoctoral experience.

Jo De Boeck, imec’s CTO says: “We are very proud that young researchers such as Niels Verellen are awarded an ERC Starting Grant and as such get a unique opportunity to fulfill their ambitions and creative ideas in research. At imec, we select and foster our young scientists and provide them with a world-class infrastructure. These ERC Starting Grants show that their work indeed meets the highest standards.”

Researchers at the National Institute of Standards and Technology (NIST) have made a silicon chip that distributes optical signals precisely across a miniature brain-like grid, showcasing a potential new design for neural networks.

NIST’s grid-on-a-chip distributes light signals precisely, showcasing a potential new design for neural networks. The three-dimensional structure enables complex routing scheme, which are necessary to mimic the brain. Light could travel farther and faster than electrical signals. Credit: Chiles/NIST

The human brain has billions of neurons (nerve cells), each with thousands of connections to other neurons. Many computing research projects aim to emulate the brain by creating circuits of artificial neural networks. But conventional electronics, including the electrical wiring of semiconductor circuits, often impedes the extremely complex routing required for useful neural networks.

The NIST team proposes to use light instead of electricity as a signaling medium. Neural networks already have demonstrated remarkable power in solving complex problems, including rapid pattern recognition and data analysis. The use of light would eliminate interference due to electrical charge and the signals would travel faster and farther.

“Light’s advantages could improve the performance of neural nets for scientific data analysis such as searches for Earth-like planets and quantum information science, and accelerate the development of highly intuitive control systems for autonomous vehicles,” NIST physicist Jeff Chiles said.

A conventional computer processes information through algorithms, or human-coded rules. By contrast, a neural network relies on a network of connections among processing elements, or neurons, which can be trained to recognize certain patterns of stimuli. A neural or neuromorphic computer would consist of a large, complex system of neural networks.

Described in a new paper, the NIST chip overcomes a major challenge to the use of light signals by vertically stacking two layers of photonic waveguides–structures that confine light into narrow lines for routing optical signals, much as wires route electrical signals. This three-dimensional (3D) design enables complex routing schemes, which are necessary to mimic neural systems. Furthermore, this design can easily be extended to incorporate additional waveguiding layers when needed for more complex networks.

The stacked waveguides form a three-dimensional grid with 10 inputs or “upstream” neurons each connecting to 10 outputs or “downstream” neurons, for a total of 100 receivers. Fabricated on a silicon wafer, the waveguides are made of silicon nitride and are each 800 nanometers (nm) wide and 400 nm thick. Researchers created software to automatically generate signal routing, with adjustable levels of connectivity between the neurons.

Laser light was directed into the chip through an optical fiber. The goal was to route each input to every output group, following a selected distribution pattern for light intensity or power. Power levels represent the pattern and degree of connectivity in the circuit. The authors demonstrated two schemes for controlling output intensity: uniform (each output receives the same power) and a “bell curve” distribution (in which middle neurons receive the most power, while peripheral neurons receive less).

To evaluate the results, researchers made images of the output signals. All signals were focused through a microscope lens onto a semiconductor sensor and processed into image frames. This method allows many devices to be analyzed at the same time with high precision. The output was highly uniform, with low error rates, confirming precise power distribution.

“We’ve really done two things here,” Chiles said. “We’ve begun to use the third dimension to enable more optical connectivity, and we’ve developed a new measurement technique to rapidly characterize many devices in a photonic system. Both advances are crucial as we begin to scale up to massive optoelectronic neural systems.”

Researchers have shown that a chip-based device measuring a millimeter square could be used to generate quantum-based random numbers at gigabit per second speeds. The tiny device requires little power and could enable stand-alone random number generators or be incorporated into laptops and smart phones to offer real-time encryption.

Researchers created a chip-based device measuring a millimeter square that can potentially generate quantum-based random numbers at gigabit per second speeds. The small square to the right of the penny contains all the optical components of the random number generator. Credit: Francesco Raffaelli, University of Bristol

“While part of the control electronics is not integrated yet, the device we designed integrates all the required optical components on one chip,” said first author Francesco Raffaelli, University of Bristol, United Kingdom. “Using this device by itself or integrating it into other portable devices would be very useful in the future to make our information more secure and to better protect our privacy.”

Random number generators are used to encrypt data transmitted during digital transactions such as buying products online or sending a secure e-mail. Today’s random number generators are based on computer algorithms, which can leave data vulnerable if hackers figure out the algorithm used.

In The Optical Society (OSA) journal Optics Express, the researchers report a quantum random number generator based on randomly emitted photons from a diode laser. Because the photon emission is inherently random, it is impossible to predict the numbers that will be generated.

“Compared to other integrated quantum random number generators demonstrated recently, ours can accomplish very high generation rates with relatively low optical powers,” said Raffaelli. “Using less power to produce random numbers helps avoid problems such as excess heat on the chip.”

Silicon photonics

The new chip was enabled by developments in silicon photonics technology, which uses the same semiconductor fabrication techniques used to make computer chips to fabricate optical components in silicon. It is now possible to fabricate waveguides into silicon that can guide light through the chip without losing the light energy along the way. These waveguides can be integrated onto a chip with electronics and integrated detectors that operate at very high speeds to convert the light signals into information.

The new chip-based random number generator takes advantage of the fact that under certain conditions a laser will emit photons randomly. The device converts these photons into optical power using a tiny device called an interferometer. Very small photodetectors integrated into the same chip then detect the optical power and convert it into a voltage that can be turned into random numbers.

“Despite the advancements in silicon photonics, there is still light lost inside the chip, which leads to very little light reaching the detectors,” said Raffaelli. “This required us to optimize all the parameters very precisely and design low noise electronics to detect the optical signal inside the chip.”

The new chip-based device not only brings portability advantages but is also more stable than the same device made using bulk optics. This is because interferometers are very sensitive to environmental conditions such as temperature and it is easier to control the temperature of a small chip. It is also far easier to precisely reproduce thousands of identical chips using semiconductor fabrication, whereas reproducing the necessary precision with bulk optics is more difficult.

Testing the chip

To experimentally test their design, the researchers had a foundry fabricate the random number generator chip. After characterizing the optical and electronic performance, they used it for random number generation. They estimate a potential randomness generation rate of nearly 2.8 gigabits per second for their device, which would be fast enough to enable real-time encryption.

“We demonstrated random number generation using about a tenth of the power used in other chip-based quantum random number generator devices,” said Raffaelli. “Our work shows the feasibility of this type of integrated platform.”

Although the chip containing the optical components is only one millimeter square, the researchers used an external laser which provides the source of randomness and electronics and measurement tools that required an optical table. They are now working to create a portable device about the size of a mobile phone that contains both the chip and the necessary electronics.

Scientists at the University of Alberta in Edmonton, Canada have created the most dense, solid-state memory in history that could soon exceed the capabilities of current hard drives by 1,000 times.

Faced with the question of how to respond to the ever-increasing needs of our data-driven society, the answer for a team of scientists was simple: more memory, less space. Finding the way to do that, however, was anything but simple, involving years of painstaking incremental advances in atomic-scale nanotechnology.

But their new discovery for atomic-scale rewritable memory–quickly removing or replacing single atoms–allows the creation of small, stable, dense memory at the atomic-scale.

To demonstrate the new discovery, Achal, Wolkow, and their fellow scientists not only fabricated the world’s smallest maple leaf, they also encoded the entire alphabet at a density of 138 terabytes, roughly equivalent to writing 350,000 letters across a grain of rice. For a playful twist, Achal also encoded music as an atom-sized song, the first 24 notes of which will make any video-game player of the 80s and 90s nostalgic for yesteryear but excited for the future of technology and society. Credit: Roshan Achal / courtesy Nature Communications

“Essentially, you can take all 45 million songs on iTunes and store them on the surface of one quarter,” said Roshan Achal, PhD student in Department of Physics at the University of Alberta and lead author on the new research. “Five years ago, this wasn’t even something we thought possible.”

Previous discoveries were stable only at cryogenic conditions, meaning this new finding puts society light years closer to meeting the need for more storage for the current and continued deluge of data. One of the most exciting features of this memory is that it’s road-ready for real-world temperatures, as it can withstand normal use and transportation beyond the lab.

“What is often overlooked in the nanofabrication business is actual transportation to an end user, that simply was not possible until now given temperature restrictions,” continued Achal. “Our memory is stable well above room temperature and precise down to the atom.”

Achal explained that immediate applications will be data archival. Next steps will be increasing readout and writing speeds, meaning even more flexible applications.

More memory, less space

Achal works with University of Alberta physics professor Robert Wolkow, a pioneer in the field of atomic-scale physics. Wolkow perfected the art of the science behind nanotip technology, which, thanks to Wolkow and his team’s continued work, has now reached a tipping point, meaning scaling up atomic-scale manufacturing for commercialization.

“With this last piece of the puzzle now in-hand, atom-scale fabrication will become a commercial reality in the very near future,” said Wolkow. Wolkow’s Spin-off company, Quantum Silicon Inc., is hard at work on commercializing atom-scale fabrication for use in all areas of the technology sector.

To demonstrate the new discovery, Achal, Wolkow, and their fellow scientists not only fabricated the world’s smallest maple leaf, they also encoded the entire alphabet at a density of 138 terabytes, roughly equivalent to writing 350,000 letters across a grain of rice. For a playful twist, Achal also encoded music as an atom-sized song, the first 24 notes of which will make any video-game player of the 80s and 90s nostalgic for yesteryear but excited for the future of technology and society.

A new manufacturing technique uses a process similar to newspaper printing to form smoother and more flexible metals for making ultrafast electronic devices.

The low-cost process, developed by Purdue University researchers, combines tools already used in industry for manufacturing metals on a large scale, but uses the speed and precision of roll-to-roll newspaper printing to remove a couple of fabrication barriers in making electronics faster than they are today.

Roll-to-roll laser-induced superplasticity, a new fabrication method, prints metals at the nanoscale needed for making electronic devices ultrafast. Credit: Purdue University image/Ramses Martinez

Cellphones, laptops, tablets, and many other electronics rely on their internal metallic circuits to process information at high speed. Current metal fabrication techniques tend to make these circuits by getting a thin rain of liquid metal drops to pass through a stencil mask in the shape of a circuit, kind of like spraying graffiti on walls.

“Unfortunately, this fabrication technique generates metallic circuits with rough surfaces, causing our electronic devices to heat up and drain their batteries faster,” said Ramses Martinez, assistant professor of industrial engineering and biomedical engineering.

Future ultrafast devices also will require much smaller metal components, which calls for a higher resolution to make them at these nanoscale sizes.

“Forming metals with increasingly smaller shapes requires molds with higher and higher definition, until you reach the nanoscale size,” Martinez said. “Adding the latest advances in nanotechnology requires us to pattern metals in sizes that are even smaller than the grains they are made of. It’s like making a sand castle smaller than a grain of sand.”

This so-called “formability limit” hampers the ability to manufacture materials with nanoscale resolution at high speed.

Purdue researchers have addressed both of these issues – roughness and low resolution – with a new large-scale fabrication method that enables the forming of smooth metallic circuits at the nanoscale using conventional carbon dioxide lasers, which are already common for industrial cutting and engraving.

“Printing tiny metal components like newspapers makes them much smoother. This allows an electric current to travel better with less risk of overheating,” Martinez said.

The fabrication method, called roll-to-roll laser-induced superplasticity, uses a rolling stamp like the ones used to print newspapers at high speed. The technique can induce, for a brief period of time, “superelastic” behavior to different metals by applying high-energy laser shots, which enables the metal to flow into the nanoscale features of the rolling stamp – circumventing the formability limit.

“In the future, the roll-to-roll fabrication of devices using our technique could enable the creation of touch screens covered with nanostructures capable of interacting with light and generating 3D images, as well as the cost-effective fabrication of more sensitive biosensors,” Martinez said.

Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The significant breakthrough demonstrates that an optical circuit can perform a critical function of an electronics-based artificial neural network and could lead to less expensive, faster and more energy efficient ways to perform complex tasks such as speech or image recognition.

Researchers have shown a neural network can be trained using an optical circuit (blue rectangle in the illustration). In the full network there would be several of these linked together. The laser inputs (green) encode information that is carried through the chip by optical waveguides (black). The chip performs operations crucial to the artificial neural network using tunable beam splitters, which are represented by the curved sections in the waveguides. These sections couple two adjacent waveguides together and are tuned by adjusting the settings of optical phase shifters (red and blue glowing objects), which act like ‘knobs’ that can be adjusted during training to perform a given task. Credit: Tyler W. Hughes, Stanford University

“Using an optical chip to perform neural network computations more efficiently than is possible with digital computers could allow more complex problems to be solved,” said research team leader Shanhui Fan of Stanford University. “This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can’t imagine now.”

An artificial neural network is a type of artificial intelligence that uses connected units to process information in a manner similar to the way the brain processes information. Using these networks to perform a complex task, for instance voice recognition, requires the critical step of training the algorithms to categorize inputs, such as different words.

Although optical artificial neural networks were recently demonstrated experimentally, the training step was performed using a model on a traditional digital computer and the final settings were then imported into the optical circuit. In Optica, The Optical Society’s journal for high impact research, Stanford University researchers report a method for training these networks directly in the device by implementing an optical analogue of the ‘backpropagation’ algorithm, which is the standard way to train conventional neural networks.

“Using a physical device rather than a computer model for training makes the process more accurate,” said Tyler W. Hughes, first author of the paper. “Also, because the training step is a very computationally expensive part of the implementation of the neural network, performing this step optically is key to improving the computational efficiency, speed and power consumption of artificial networks.”

A light-based network

Although neural network processing is typically performed using a traditional computer, there are significant efforts to design hardware optimized specifically for neural network computing. Optics-based devices are of great interest because they can perform computations in parallel while using less energy than electronic devices.

In the new work, the researchers overcame a significant challenge to implementing an all-optical neural network by designing an optical chip that replicates the way that conventional computers train neural networks.

An artificial neural network can be thought of as a black box with a number of knobs. During the training step, these knobs are each turned a little and then the system is tested to see if the performance of the algorithms improved.

“Our method not only helps predict which direction to turn the knobs but also how much you should turn each knob to get you closer to the desired performance,” said Hughes. “Our approach speeds up training significantly, especially for large networks, because we get information about each knob in parallel.”

On-chip training

The new training protocol operates on optical circuits with tunable beam splitters that are adjusted by changing the settings of optical phase shifters. Laser beams encoding information to be processed are fired into the optical circuit and carried by optical waveguides through the beam splitters, which are adjusted like knobs to train the neural network algorithms.

In the new training protocol, the laser is first fed through the optical circuit. Upon exiting the device, the difference from the expected outcome is calculated. This information is then used to generate a new light signal, which is sent back through the optical network in the opposite direction. By measuring the optical intensity around each beam splitter during this process, the researchers showed how to detect, in parallel, how the neural network performance will change with respect to each beam splitter’s setting. The phase shifter settings can be changed based on this information, and the process may be repeated until the neural network produces the desired outcome.

The researchers tested their training technique with optical simulations by teaching an algorithm to perform complicated functions, such as picking out complex features within a set of points. They found that the optical implementation performed similarly to a conventional computer.

“Our work demonstrates that you can use the laws of physics to implement computer science algorithms,” said Fan. “By training these networks in the optical domain, it shows that optical neural network systems could be built to carry out certain functionalities using optics alone.”

The researchers plan to further optimize the system and want to use it to implement a practical application of a neural network task. The general approach they designed could be used with various neural network architectures and for other applications such as reconfigurable optics.

Boston Semi Equipment (BSE), a global semiconductor test handler manufacturer and provider of test automation technical services, introduced today its Zeus gravity feed solution for handling pressure MEMS devices that require pressure and vacuum stimulus during testing. The system is an enhanced capability for BSE’s existing pressure MEMS handling solution and enables MEMS test cells to apply pressure and vacuum in a single test cycle.

“Our innovative design for applying a pressure stimulus to devices under test enabled us to easily integrate a vacuum stimulus,” said Kevin Brennan, vice president of marketing for BSE. “This solution is unique in the industry. Our customers can already test MEMS devices faster using the Zeus handler, and now they can test with both vacuum and pressure stimuli in a single pass through the handler. This capability is a significant boost to productivity, making Zeus-based MEMS test cells a highly cost-effective solution for pressure MEMS testing.”

The Zeus is a tri-temperature handler that can be configured with up to eight test sites. Cold temperature testing is achieved using LN2 or a BSE-designed, two-stage chiller, the MR2. The Zeus offers the features and performance needed by today’s test cells at a more affordable price point.

Kirigami (also called “paper-cuts” or “jianzhi”) is one of the most traditional Chinese folk arts. It is widely used in window decorations, gift cards, festivals, and ceremonies, etc. Kirigami involves cutting and folding flat objects into 3D shapes. Recently, the techniques of this ancient art have been used in various scientific and technological fields, including designs for solar arrays, biomedical devices and micro-/nano- electromechanical systems (MEMS/NEMS).

Macroscopic paper-cuts in a paper sheet and nano-kirigami in an 80-nm thick gold film. Credit: Institute of Physics

Dr. LI Jiafang, from the Institute of Physics (IOP), Chinese Academy of Sciences, has recently formed an international team to apply kirigami techniques to advanced 3D nanofabrication.

Inspired by a traditional Chinese kirigami design called “pulling flower,” the team developed a direct nano-kirigami method to work with flat films at the nanoscale. They utilized a focused ion beam (FIB) instead of knives/scissors to cut a precise pattern in a free-standing gold nanofilm, then used the same FIB, instead of hands, to gradually “pull” the nanopattern into a complex 3D shape.

The “pulling” forces were induced by heterogeneous vacancies (introducing tensile stress) and the implanted ions (introducing compressive stress) within the gold nanofilm during FIB irradiation.

By utilizing the topography-guided stress equilibrium within the nanofilm, versatile 3D shape transformations such as upward buckling, downward bending, complex rotation and twisting of nanostructures were precisely achieved.

While previous attempts to create functional kirigami devices have used complicated sequential procedures and have been primarily aimed at realizing mechanical rather than optical functions, this new nano-kirigami method, in contrast, can be implemented in a single fabrication step and could be used to perform a number of optical functions.

For a proof-of-concept demonstration, the team produced a 3D pinwheel-like structure with giant optical chirality. The nanodevice achieved efficient manipulation of “left-handed” and “right-handed” circularly polarized light and exhibited strong uniaxial optical rotation effects in telecommunication wavelengths.

In this way, the team demonstrated a multidisciplinary connection between the two fields of nanomechanics and nanophotonics. This may represent a brand new direction for emerging kirigami research.

The team also developed a theoretical model to elucidate the dynamics during the nano-kirigami fabrication. This is of great significance since it allows researchers to design 3D nanogeometries based on desired optical functionalities. In contrast, previous studies relied heavily on intuitive designs.

In other words, in terms of geometric design, nano-kirigami offers an intelligent 3D nanofabrication method beyond traditional bottom-up, top-down and self-assembly nanofabrication techniques.

Its concept can be extended to broad nanofabrication platforms and could lead to the realization of complex optical nanostructures for sensing, computation, micro-/nano- electromechanical systems or biomedical devices.

This work, entitled “Nano-kirigami with giant optical chirality,” was published in Science Advances on July 6, 2018.

Directly converting electrical power to heat is easy. It regularly happens in your toaster, that is, if you make toast regularly. The opposite, converting heat into electrical power, isn’t so easy.

Researchers from Sandia National Laboratories have developed a tiny silicon-based device that can harness what was previously called waste heat and turn it into DC power. Their advance was recently published in Physical Review Applied.

This tiny silicon-based device developed at Sandia National Laboratories can catch and convert waste heat into electrical power. The rectenna, short for rectifying antenna, is made of common aluminum, silicon and silicon dioxide using standard processes from the integrated circuit industry. Credit: Photo by Randy Montoya/Sandia National Laboratories

“We have developed a new method for essentially recovering energy from waste heat. Car engines produce a lot of heat and that heat is just waste, right? So imagine if you could convert that engine heat into electrical power for a hybrid car. This is the first step in that direction, but much more work needs to be done,” said Paul Davids, a physicist and the principal investigator for the study.

“In the short term we’re looking to make a compact infrared power supply, perhaps to replace radioisotope thermoelectric generators.” Called RTGs, the generators are used for such tasks as powering sensors for space missions that don’t get enough direct sunlight to power solar panels.

Davids’ device is made of common and abundant materials, such as aluminum, silicon and silicon dioxide — or glass — combined in very uncommon ways.

Silicon device catches, channels and converts heat into power

Smaller than a pinkie nail, the device is about 1/8 inch by 1/8 inch, half as thick as a dime and metallically shiny. The top is aluminum that is etched with stripes roughly 20 times smaller than the width of a human hair. This pattern, though far too small to be seen by eye, serves as an antenna to catch the infrared radiation.

Between the aluminum top and the silicon bottom is a very thin layer of silicon dioxide. This layer is about 20 silicon atoms thick, or 16,000 times thinner than a human hair. The patterned and etched aluminum antenna channels the infrared radiation into this thin layer.

The infrared radiation trapped in the silicon dioxide creates very fast electrical oscillations, about 50 trillion times a second. This pushes electrons back and forth between the aluminum and the silicon in an asymmetric manner. This process, called rectification, generates net DC electrical current.

The team calls its device an infrared rectenna, a portmanteau of rectifying antenna. It is a solid-state device with no moving parts to jam, bend or break, and doesn’t have to directly touch the heat source, which can cause thermal stress.

Infrared rectenna production uses common, scalable processes

Because the team makes the infrared rectenna with the same processes used by the integrated circuit industry, it’s readily scalable, said Joshua Shank, electrical engineer and the paper’s first author, who tested the devices and modeled the underlying physics while he was a Sandia postdoctoral fellow.

He added, “We’ve deliberately focused on common materials and processes that are scalable. In theory, any commercial integrated circuit fabrication facility could make these rectennas.”

That isn’t to say creating the current device was easy. Rob Jarecki, the fabrication engineer who led process development, said, “There’s immense complexity under the hood and the devices require all kinds of processing tricks to build them.”

One of the biggest fabrication challenges was inserting small amounts of other elements into the silicon, or doping it, so that it would reflect infrared light like a metal, said Jarecki. “Typically you don’t dope silicon to death, you don’t try to turn it into a metal, because you have metals for that. In this case we needed it doped as much as possible without wrecking the material.”

The devices were made at Sandia’s Microsystems Engineering, Science and Applications Complex. The team has been issued a patent for the infrared rectenna and have filed several additional patents.

The version of the infrared rectenna the team reported in Physical Review Applied produces 8 nanowatts of power per square centimeter from a specialized heat lamp at 840 degrees. For context, a typical solar-powered calculator uses about 5 microwatts, so they would need a sheet of infrared rectennas slightly larger than a standard piece of paper to power a calculator. So, the team has many ideas for future improvements to make the infrared rectenna more efficient.

Future work to improve infrared rectenna efficiency

These ideas include making the rectenna’s top pattern 2D x’s instead of 1D stripes, in order to absorb infrared light over all polarizations; redesigning the rectifying layer to be a full-wave rectifier instead of the current half-wave rectifier; and making the infrared rectenna on a thinner silicon wafer to minimize power loss due to resistance.

Through improved design and greater conversion efficiency, the power output per unit area will increase. Davids thinks that within five years, the infrared rectenna may be a good alternative to RTGs for compact power supplies.

Shank said, “We need to continue to improve in order to be comparable to RTGs, but the rectennas will be useful for any application where you need something to work reliably for a long time and where you can’t go in and just change the battery. However, we’re not going to be an alternative for solar panels as a source of grid-scale power, at least not in the near term.”

Davids added, “We’ve been whittling away at the problem and now we’re beginning to get to the point where we’re seeing relatively large gains in power conversion, and I think that there’s a path forward as an alternative to thermoelectrics. It feels good to get to this point. It would be great if we could scale it up and change the world.”