Category Archives: Applications

In a key move to inspire the next generation of innovators, the School District of Osceola County (SDOC) today became the first school district to join the SEMI High Tech U (HTU) Certified Partner Program (CPP), a curriculum that prepares high-school students to pursue careers in STEM fields.

Under the program sponsored by the SEMI Foundation, SDOC will independently deliver HTU programs to local students at the Osceola Technical College Campus, in Kissimmee, Florida. SEMI Foundation awarded SDOC the certification today at a graduation ceremony for HTU students.

“SDOC’s partnership with the SEMI Foundation gives young people and families in our community exposure to high-tech career opportunities and the educational pathways to reach their goals,” said Debra Pace, superintendent of School District of Osceola County. “Our industry partners – including Mercury, University of Central Florida, BRIDG, Osceola Technical College, imec, Neo City and the Osceola County Education Foundation – have all made it possible for SDOC to offer this amazing opportunity to students.”

“We are delighted to partner with SDOC in our common goal to motivate the next generation of innovators,” said Leslie Tugman, executive director of the SEMI Foundation. “The School District of Osceola County is well-positioned to put college-bound high school students on a track that speeds the time from graduation to employment in high technology. SDOC’s certification is a tremendous benefit for it students, the community and employers in the fast-growing Central Florida tech corridor.”

To win the certification, SDOC delivered HTU over the past three years with guidance and instruction from SEMI. SDOC is only the second organization to receive the certification.

The nonprofit SEMI Foundation has been delivering its flagship program, SEMI High Tech U, at industry sites around the world since 2001 to emphasize the importance of STEM skills and inspire young people to pursue careers in high-technology fields. HTU students meet engineers and STEM volunteer instructors from industry for site tours and hands-on classroom activities such as etching wafers, making circuits, coding and training for professional interviews.

SEMI’s Certified Partner Program identifies organizations that provide quality training and can recruit and educate local high-school students in the value of careers in science, technology, engineering and math (STEM). Participating organizations are trained to deliver the unique SEMI curriculum with the support of volunteer instructors from the high-tech and STEM industries. SEMI High Tech U is the longest-running STEM career exploration program in the United States with documented student impact. Since inception, SEMI has reached over 8,000 high-school students in 12 states and nine countries with its award-winning program.

SEMI Foundation is a 501(c)(3) nonprofit charitable organization founded in 2001 to support education and career awareness in the electronics and high-tech fields through career exploration programs and scholarships. For more information, visit www.semifoundation.org.

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.

Applied Materials, Inc. today announced it has been awarded a contract by the Defense Advanced Research Projects Agency (DARPA) to develop a new type of electronic switch for artificial intelligence that mimics the way the human brain works to enable dramatic improvements in performance and power efficiency. The project is being supported by DARPA’s Electronics Resurgence Initiative, a multi-year research effort intended to achieve far-reaching improvements in electronics performance well beyond the limits of traditional Moore’s Law scaling.

Applied is working with Arm and Symetrix to develop a new neuromorphic switch based on CeRAM memory that can allow data to be stored and processed in the same material. The goal of the project is to enable a major improvement in artificial intelligence compute performance and power efficiency with the use of analog signal processing as compared to current digital approaches.

“This project is a perfect example of how new materials and architectures can be developed to enable new ways to accelerate artificial intelligence applications as classic Moore’s Law scaling slows,” said Steve Ghanayem, senior vice president of New Markets and Alliances at Applied Materials. “Applied has the industry’s broadest portfolio in materials engineering capabilities and is excited to be part of a team enabling breakthroughs for artificial intelligence.”

Today’s announcement was part of DARPA’s first annual ERI Summit in San Francisco. Applied Materials’ president and CEO, Gary Dickerson, delivered a keynote speech at the event highlighting the need for materials innovation in the AI era and calling for a new level of industry connectivity to speed progress across materials engineering, design and manufacturing.

Announced in September 2017, the ERI Materials & Integration programs seek to answer this question: Can we use the integration of unconventional electronics materials to enhance conventional silicon circuits and continue the progress in performance traditionally associated with scaling?

The Applied Materials team is part of the ERI Foundations Required for Novel Compute (FRANC) program, which seeks innovations that go beyond von Neumann compute architectures. Central is the design of circuits that leverage the properties of new materials and integration schemes to process data in ways that eliminate or minimize data movement. The novel compute topologies that come out of this effort could allow processing to happen where the data is stored with structures that are radically different from conventional digital logic processors, ultimately allowing for significant gains in compute performance.

Applied Materials, Inc. (Nasdaq:AMAT) is a developer of materials engineering solutions used to produce virtually every new chip and advanced display in the world.

Park Systems announced the opening of the Park Nanoscience Lab at the prestigious Indian Institute of Science (IISC) Bangalore India, which has been upgraded to the status of Institute of Eminence.

The Nanoscience Lab will be equipped with Park NX20 AFM at the Centre for Nano Science and Engineering (CeNSE) and will hold workshops and symposiums on the latest advancements in nanometrology and offer researchers a chance to experience the latest in AFM technology.

The official inauguration ceremony of the Park Nanoscience Lab in India will be held on Wednesday July 25, 2018 at 10 AM featuring a talk by Dr. San Joon Cho of Park Systems Corporation,who will make an official presentation, declaring the Park NanoScience Lab, a national facility where researchers will have access to Park Systems cutting-edge Atomic Force Microscopes with high resolution nanoscale imaging.The event will also include an AFM live demonstration and is open to the press and public. To register to attend go to: http://www.parksystems.com/iisc

“We are honored to have the Park Nanoscience Lab here at Indian Institute of Science,” The Director, CeNSE- Indian Institute of Science further added, “The partnership with Park Systems and their Atomic Force Microscope technology strengthens our academic and scientific community by bringing an exciting new research tool to a shared access location, supporting the growing demand for nanotechnology here in India.”

The Park Nanoscience Labwill showcase advanced atomic force microscopy systems, demonstrate a wide variety of applications ranging from materials, to chemical and biological to semiconductor and devices, and provide hands on experience, training and service, year-round.

“Increasingly, AFM is being selected for Nanotechnology research over other metrology techniques due to its non-destructive measurement and sub-nanometer accuracy,” states Dr. Sang-il Park, Park Systems Chairman and CEO. “The new Park Nanoscience Lab at Indian Institute is a tremendous step forward for researchers in India who work in the advancing fields of nano science and technology.”

Park Systems advanced AFM platform includes SmartScan, an innovative and pioneering AFM intelligence that produces high quality imaging with very few clicks. Park SmartScan’s unique design opens up the power of AFM to everyone and drastically boosts the productivity of all users.

Since going public and listing on KOSDAQ in 2016, Park Systems’ stock has quadrupled as they continue to lead the world in growing AFM market share. Park Systems, a global AFM manufacturer, has Nanoscience Centers in key cities world-wide including Santa Clara, CA, Albany NY, Tokyo, Japan, Singapore, Heidelberg, Germany, Suwon and Seoul.

Toshiba Memory Corporation today announced that it has developed a prototype sample of 96-layer BiCS FLASH, its proprietary 3D flash memory, with 4-bit-per-cell (quad level cell, QLC) technology that boosts single-chip memory capacity to the highest level yet achieved.

Toshiba Memory will start to deliver samples to SSD and SSD controller manufacturers for evaluation from the beginning of September, and expects to start mass production in 2019.

The advantage of QLC technology is pushing the bit count for data per memory cell from three to four and significantly expanding capacity. The new product achieves the industry’s maximum capacity [1] of 1.33 terabits for a single chip which was jointly developed with Western Digital Corporation.

This also realizes an unparalleled capacity of 2.66 terabytes with a 16-chip stacked architecture in one package. The huge volumes of data generated by mobile terminals and the like continue to increase with the spread of SNS and progress in IoT, and the need to analyze and utilize that data in real time is expected to increase dramatically. That will require even faster than HDD, larger capacity storage and QLC products using the 96-layer process will contribute a solution.

A packaged prototype of the new device will be exhibited at the 2018 Flash Memory Summit in Santa Clara, California, USA from August 6th to 9th.

Looking to the future, Toshiba Memory will continue to improve memory capacity and performance and to develop 3D flash memories that meet diverse market needs, including the fast expanding data center storage market.

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.

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.

By Yoichiro Ando

The Japan semiconductor manufacturing supply chain is a global semiconductor industry workhorse, producing about one third of world’s chip equipment and more than half of its semiconductor materials. In contributing the vast majority of these products, SEMI Japan member companies hold the high distinction of enabling continuous development of the worldwide semiconductor industry. Aptly, then, technology powerhouses IBM, Nissan Motors and Toshiba offered insights into the latest trends and innovations in computing and smart cars at the late-May SEMI Japan Members Days in Tokyo with 133 technologists from member companies in attendance.

As the audience discovered, chip innovation never sleeps and, as futuristic as it can be, invariably gives rise to possibilities beyond the human imagination. That was the message of kickoff presentation “Computing Reimagined – AI/Quantum/IoT” – by Dr. Shintaro Yamamichi, Senior Manager, Science & Technology at IBM Research-Tokyo. Dr. Yamamichi cited three examples of how semiconductors uncover new technology frontiers.

  • Computational materials discovery, a novel methodology, is the application of theory and computation to unearthing new materials and the key to enabling an ongoing stream of semiconductor innovation. In particular, using cognitive technology to mine huge volumes of literature reveal new insights into materials that uncover even more functionality such as greater conductivity and heat resistance. With new materials the oxygen of ever more advanced semiconductor chip manufacturing, the semiconductor industry will surely benefit from this methodology.
  • The opportunity to accelerate quantum computing innovation is now. Launched in May 2016, the IBM Quantum Experience gives students, researchers and general science enthusiasts hands-on access to IBM’s experimental cloud-enabled quantum computing platform. The online platform features a forum for discussing quantum computing topics, tutorials on how to program IBM Q devices, and other educational material about quantum computing. Dr. Yamamichi encouraged the audience to join the program.
  • The world’s tiniest computer, unveiled by IBM at the company’s Think 2018 conference in Las Vegas, packs several hundred thousand transistors and, IBM claims, the equivalent power of a 1990s x86 chip into a package smaller than a grain of salt. The computer’s small form factor (less than 1mm x 1mm) and low manufacturing cost means it can be embedded in product price tags and packages as an anti-fraud device using blockchain technology.

Vehicles need to be both electric and intelligent as countries become more populous and traffic density increases. More drivers extend average drive time, boost greenhouse emissions, devour precious energy resources and lead to more traffic congestion and accidents. Dr. Haruyoshi Kumura, fellow at Nissan Motor, highlighted these issues in stressing the importance of a new era of intelligent mobility. To mitigate these problems, Nissan is focusing on the electrification and intelligence of its vehicles:

  • Nissan’s electric vehicle, Leaf, reduces accidents with electric intelligence systems such as e-Pedal, which uses an accelerator pedal only for both acceleration and deceleration, and ProPILOT Park, a feature that automatically parks the car by using multiple cameras and ultrasonic sonars to detect pedestrians and other objects around the vehicle.

  • With more than 90 percent of traffic accidents caused by driver error, Nissan plans to introduce autonomous driving on multi-lane highways by the end of 2018 and on city streets by 2020. By 2022, the company plans to roll out full autonomous driving to reduce traffic accidents caused by inattentive drivers.
  • For full autonomous driving to materialize, sensor fusion technology must incorporate a combination of technologies – radar systems, light detection and ranging (LiDAR) systems and cameras – to identify the shapes and locations of nearby moving objects and measure their speed. Sensed information is then processed by a 3D graphic analyzer to make electric throttle, braking and steering decisions.

The outlook for automotive industry includes car sharing and more electrification – both insights from Yoshiki Hayakashi, general manager, automotive solution strategic planning division at Toshiba Electronic Devices & Storage, who offered his perspectives on trends in Japan’s automotive industry and beyond.

  • To meet the requirements of the COP21 Paris agreement, the global automotive industry is shifting to electrification. Toshiba estimates 60 percent of new cars will be electric vehicles by 2040 to meet the International Energy Agency’s global EV outlook.
  • In Japan, autonomous driving or advanced driver assistance systems (ADAS) will be offered in certain areas by 2020, the year of the Tokyo Olympic games. Growth of these advanced driving systems hinges on infrastructure development. Supporting data centers, intelligent transport systems, vehicle-to-everything connections, and smart city are all necessary components.
  • Car ownership will begin to cede ground to car sharing with technology elites such as Tesla, Apple and Google leading the way. To expand the car-sharing industry, new alliances will take shape between new and old-guard automotive companies and electronics manufacturing services (EMS) providers.
  • Autonomous driving requires precise 3D renderings of actual roadways using sensors for route mapping. While sensor fusion must be deployed for these capabilities, LiDAR offers better sensing range and space resolution precision than ultrasonic sonars, radars, and cameras.

The next SEMI Japan members day is scheduled for October 30 in Tokyo. SEMI holds similar events in most regions where SEMI and its members operate. For the members events in your region, contact the SEMI office nearest you.

Yoichiro Ando is a marketing director in SEMI Japan.

Originally published on the SEMI blog.

Billions of objects ranging from smartphones and watches to buildings, machine parts and medical devices have become wireless sensors of their environments, expanding a network called the “internet of things.”

As society moves toward connecting all objects to the internet – even furniture and office supplies – the technology that enables these objects to communicate and sense each other will need to scale up.

Researchers at Purdue University and the University of Virginia have developed a new fabrication method that makes tiny, thin-film electronic circuits peelable from a surface. The technique not only eliminates several manufacturing steps and the associated costs, but also allows any object to sense its environment or be controlled through the application of a high-tech sticker.

Eventually, these stickers could also facilitate wireless communication. The researchers demonstrate capabilities on various objects in a paper recently published in the Proceedings of the National Academy of Sciences. A YouTube video is available at https://youtu.be/8tNrPVi4OGg.

“We could customize a sensor, stick it onto a drone, and send the drone to dangerous areas to detect gas leaks, for example,” said Chi Hwan Lee, Purdue assistant professor of biomedical engineering and mechanical engineering.

Most of today’s electronic circuits are individually built on their own silicon “wafer,” a flat and rigid substrate. The silicon wafer can then withstand the high temperatures and chemical etching that are used to remove the circuits from the wafer.

But high temperatures and etching damage the silicon wafer, forcing the manufacturing process to accommodate an entirely new wafer each time.

Lee’s new fabrication technique, called “transfer printing,” cuts down manufacturing costs by using a single wafer to build a nearly infinite number of thin films holding electronic circuits. Instead of high temperatures and chemicals, the film can peel off at room temperature with the energy-saving help of simply water.

“It’s like the red paint on San Francisco’s Golden Gate Bridge – paint peels because the environment is very wet,” Lee said. “So in our case, submerging the wafer and completed circuit in water significantly reduces the mechanical peeling stress and is environmentally-friendly.”

A ductile metal layer, such as nickel, inserted between the electronic film and the silicon wafer, makes the peeling possible in water. These thin-film electronics can then be trimmed and pasted onto any surface, granting that object electronic features.

Putting one of the stickers on a flower pot, for example, made that flower pot capable of sensing temperature changes that could affect the plant’s growth.

Lee’s lab also demonstrated that the components of electronic integrated circuits work just as well before and after they were made into a thin film peeled from a silicon wafer. The researchers used one film to turn on and off an LED light display.

“We’ve optimized this process so that we can delaminate electronic films from wafers in a defect-free manner,” Lee said.

This technology holds a non-provisional U.S. patent. The work was supported by the Purdue Research Foundation, the Air Force Research Laboratory (AFRL-S-114-054-002), the National Science Foundation (NSF-CMMI-1728149) and the University of Virginia.

Australian scientists have achieved a new milestone in their approach to creating a quantum computer chip in silicon, demonstrating the ability to tune the control frequency of a qubit by engineering its atomic configuration. The work has been published in Science Advances.

A team of researchers from the Centre of Excellence for Quantum Computation and Communication Technology (CQC2T) at UNSW Sydney have successfully implemented an atomic engineering strategy for individually addressing closely spaced spin qubits in silicon.

The frequency spectrum of an engineered molecule. The three peaks represent three different configurations of spins within the atomic nuclei, and the distance between the peaks depends on the exact distance between atoms forming the molecule. Credit: Dr. Sam Hile

The researchers built two qubits – one an engineered molecule consisting of two phosphorus atoms with a single electron, and the other a single phosphorus atom with a single electron – and placed them just 16 nanometres apart in a silicon chip.

By patterning a microwave antenna above the qubits with precision alignment, the qubits were exposed to frequencies of around 40GHz. The results showed that when changing the frequency of the signal used to control the electron spin, the single atom had a dramatically different control frequency compared to the electron spin in the molecule of two phosphorus atoms.

The UNSW researchers collaborated closely with experts at Purdue University, who used powerful computational tools to model the atomic interactions and understand how the position of the atoms impacted the control frequencies of each electron even by shifting the atoms by as little as one nanometre.

“Individually addressing each qubit when they are so close is challenging,” says UNSW Scientia Professor Michelle Simmons, Director CQC2T and co-author of the paper.

“The research confirms the ability to tune neighbouring qubits into resonance without impacting each other.”

Creating engineered phosphorus molecules with different separations between the atoms within the molecule allows for families of qubits with different control frequencies. Each molecule can be operated individually by selecting the frequency that controls its electron spin.

“We can tune into this or that molecule – a bit like tuning in to different radio stations,” says Sam Hile, lead co-author of the paper and Research Fellow at UNSW.

“It creates a built-in address which will provide significant benefits for building a silicon quantum computer.”

Tuning in and individually controlling qubits within a 2 qubit system is a precursor to demonstrating the entangled states that are necessary for a quantum computer to function and carry out complex calculations.

These results show how the team – led by Professor Simmons – have further built on their unique Australian approach of creating quantum bits from precisely positioned individual atoms in silicon.

By engineering the atomic placement of the atoms within the qubits in the silicon chip, the molecules can be created with different resonance frequencies. This means that controlling the spin of one qubit will not affect the spin of the neighbouring qubit, leading to fewer errors – an essential requirement for the development of a full-scale quantum computer.

“The ability to engineer the number of atoms within the qubits provides a way of selectively addressing one qubit from another, resulting in lower error rates even though they are so closely spaced,” says Professor Simmons.

“These results highlight the ongoing advantages of atomic qubits in silicon.”

This latest advance in spin control follows from the team’s recent research into controllable interactions between two qubits.