By: Dave Lammers

I first met Sorin Voinigescu in 1995, when – with a newly minted Ph.D. in hand — he was at the International Electron Devices Meeting (IEDM) presenting some of the early work on RF circuits crafted in CMOS technology.

Nearly 24 years later, Voinigescu is doing equally innovative pathway research in quantum computing, using the 22FDX® process from GLOBALFOUNDRIES (GF) to investigate how to integrate qubits with the RF control and readout circuits. And Voinigescu sees a type of Moore’s Law for quantum devices, in which scaled-down qubits and support circuits are able to operate at higher temperatures, perhaps obviating the need for the scarce helium consumed in today’s cryogenics.

Quantum devices today are largely Josephson Junction superconducting devices operating at milliKelvin temperatures, with wires connecting the qubits to the control and measurement electronics. Voinigescu’s lab at the University of Toronto is studying how to create semiconductor-type qubits that could be controlled with millimeter wave signals. The present-day superconducting qubits have quantum energy separation levels in the 5-10 GHz range. In order to operate the quantum gate, the microwave control signals need to be at that frequency, the 5-10 GHz range.

“All qubits, regardless of implementation, mimic a spin, and the control is performed with a signal that must resonate with the electron spin resonance frequency of that qubit,” Prof. Voinigescu explained. One way of thinking of it, he said, is that each quantum gate could require the equivalent of a 5G cellular signal, perhaps in the 60 GHz range. In fact, he was attracted to the field of quantum computing a few years ago, when he attended a session on quantum computing at IEDM and realized that his two decades of research in high-frequency circuits could play a role in the quantum computing field.

22FDX at 3.3 degrees Kelvin

There is what he calls a “trinity” in the search for higher-temperature quantum computing, where devices must be isolated from any heat or disturbances. The smaller the gate width of the transistor, the higher the frequency required to excite the qubit gate, and the higher temperature it can be operated at. Now, the 22FDX-based devices the Voinigescu lab is investigating have a gate width of 50 nm (the gate length is 18nm, and the channel thickness is 6-7nm). As the gate width is reduced, a somewhat higher temperature environment can be used for the qubits, control, and measurement circuits.

And the cool (excuse the pun) thing about the 22FDX process is something the Toronto lab and its partners recently discovered: at the extremely low temperatures required of quantum systems, the performance of the active and passive high-frequency devices actually improves.

The University of Toronto team, working with GF and industrial partners Lake Shore Cryotronics and Keysight Technologies, among others, reported at the 2019 RFIC conference, held in Boston in June,  how the 22FDX process was used to create monolithically integrated double quantum dots with readout transimpedance amplifiers (TIAs), with the output matched to 50 Ω.

More importantly for circuit design, the researchers found that the high-frequency performance of all the active and passive devices created in a production-type 22nm 22FDX technology improved at 3.3 degrees K, with no variance of the polysilicon resistors and improved quality factor of the MOM capacitors.

“What is unique to FD-SOI is that at low and high frequencies, the circuits are not affected by de-ionization, as bulk MOSFETs are known to be affected. Because of that we essentially get significantly better performance at low temperatures, as measured down to 2 degrees Kelvin. In fact, we see significant improvements down to 60-70 degrees K, and below that performance essentially remains flat,” he said. Transconductance, mobility, and fmax all improved, and that has important implications for space, satellites, and other low-temperature environments as well.

At low temperatures, threshold voltages increase for n-MOSFETS and decrease for p-MOSFETs, regardless of the technology. With FD-SOI, the back gate can be used to adjust the Vts to the optimal operation point. Circuits can be designed at room temperature, and then at low temperatures can be “validated,” tuning the Vt’s with back-gate biasing. Circuits that find a “sweet spot” at room temperature, Voinigescu said, can maintain that current density down to 2 degrees Kelvin.

Smaller Dimensions Help Raise Temps

Jamie Schaeffer, the product offering manager for the 22FDX and 12FDX FD-SOI platforms at GF, said the qubits are created in the six or seven-nanometer active layer, providing confinement for Coulomb and spin blockade devices that are, in a sense, boxed in by the buried oxide. “We have to get the spin layers to interact, and with more advanced dimensions we can get those closer. As we are going from 22 to 12 FDX, the smaller dimensions serve the goal of higher temperature quantum computing,” Schaeffer said.

Nigel Cave, a technologist who works in the CTO office at GF, said as semiconductor-type qubits are scaled to smaller dimensions, it may be possible to bring the quantum system’s operating temperature above 4 degrees Kelvin, from the 10-100 milliKelvin in today’s systems. This would enable the use of a standard helium cryostat versus a dilution cryostat, thus reducing costs and also allowing 1-2 watts of total power to be removed from the system. “The ability to remove more power potentially paves the way to co-integrate the Qubits and their control circuitry in the same FDX based device” Cave said.

Schaeffer said IBM, Google, Intel, Microsoft, and others have large quantum research programs underway. “In our case, we believe we can contribute something that is enabling for our partners who are doing meaningful work in the quantum sciences. We have a toolset that is manufacturable and leveraging our process integration capabilities is a way to get to lower costs.”

Two Camps in Quantum Ecosystem

Ted Letavic, a vice president and senior fellow at GF, said from a ten-thousand-foot level, the quantum compute community can be divided into two camps: those who are pushing for ways to create thousands of qubits in order to increase the quantum compute power; and a camp that argues more attention needs to be paid to how to use the roughly 50-100 qubit systems that now exist in order to solve real-world problems.

“One faction says we need thousands of qubits, the other faction says we have 50-100 qubit systems now and don’t know what to do with them. One answer is to provide free access in consortia, and together we can best figure out how to use them, how to create economic value and advance our economy,” he said.

GF has “some key technologies that can help,” acting as a foundry for startups, universities, and others as they investigate different approaches. Letavic, along with Cave and John Pellerin, deputy CTO and vice president of worldwide R&D, provided input to the Department of Energy, which earlier this year put out a Request for Information regarding how best to organize the Quantum Information Science Centers (QISCs).

They argued that while current exploratory R&D is largely being done in non-standard university labs, GF could provide a process integration and early manufacturing effort for researchers, startups, and others participating in the QISCs. Working with foundries would ensure that “devices intended to unlock the promise of quantum systems can be fabricated in volume within existing manufacturing assets.”

Letavic pointed to the work being done with Prof. Voinigescu as one real-life example, where the FD-SOI devices proved advantageous for I/O at 4 degrees Kelvin, and hold promise as a source of qubit transistors confined in the very thin FD-SOI layer. The Toronto effort used wafer shuttles that were processed at GF’s Dresden, Germany fab.

GF also has a silicon germanium platform, as well as a silicon photonics capability, that could play a role in “unlocking the promise of quantum.”

“I do believe in quantum, but it is going to be additive to classical compute,” Letavic said. “The society that gets to a quantum compute infrastructure first will have a very large economic advantage over the rest. And whether you are in the camp of ‘let’s chase the maximum number of qubits,’ or the camp that says ‘let’s figure out how to best use quantum systems to the best of our ability,’ GF is playing in both.”

By: Gary Dagastine

The first of a three-part series looking back at GF’s first 10 years, and ahead at the next decade and beyond.

As GLOBALFOUNDRIES celebrates its 10-year anniversary, the company finds itself at a key inflection point. Driven by financial imperatives and changing business opportunities, CEO Tom Caulfield has initiated a sweeping strategic transformation. The move is designed to better utilize the company’s resources and grow the return on investment by focusing on applications where GF’s diverse, differentiated technologies offer significant advantages.

To accomplish this it’s essential that GF employees share common goals, and a company-wide effort to facilitate that, called ONEGF, is taking place. But that isn’t an easy process for any company, and it’s harder still when you consider GF’s origins: It began as an spinoff of AMD’s in-house manufacturing operations in Dresden, Germany; then, GF acquired the Chartered Semiconductor foundry in Singapore; built a greenfield foundry in Malta, NY; and, as if that wasn’t enough, acquired IBM’s former in-house technology development group and chip manufacturing operations in New York and Vermont.

Given all of the change that has taken place, Foundry Files wanted to get the perspectives of long-time employees from various parts of the company on how GF got where it is today, to learn if there are lessons in that experience which can help with the challenges that lie ahead.

Read on to learn why the following GF employees say that a sense of shared purpose, a laser-like focus on the customer, and finding satisfaction in different types of technological innovations may well be the keys to success in the company’s next decade.

Learning by Doing in Dresden: From an IDM to a Foundry

“We can show plenty of scars,” said Jens Drews, Director of Communications & Government Relations for Fab 1 in Dresden, referring to the fab’s challenging transition from a dedicated manufacturing resource for AMD microprocessors to a foundry satisfying the wide-ranging demands of new customers, while at the same time ramping multiple new technologies.

Asked to describe the site’s journey over the last ten years, Jens thought for a moment and then came back with the opening line of Charles Dickens’ A Tale of Two Cities: “It was the best of times, it was the worst of times, … it was the season of light, it was the season of darkness; it was the spring of hope, it was the winter of despair,” adding that the fab’s momentum and growth potential clearly pointed towards the “best of times.”

Jens, the voice of GF’s Dresden site to employees and to the German and European media and governments, is in a position to know because he has been with the site for more 23 years and has witnessed the changes firsthand.

“We started out in Dresden with a straightforward goal – to compete with Intel in CPUs – and we were tremendously proud when we were able to do that,” he said. “But during the last few years when we were still a part of AMD, the chip industry went through a big paradigm shift away from the original IDM model towards a model that saw the rise of fabless and fab-light companies and foundries. For a while, AMD Dresden continued to be an island of stability in a sea of change, and we were going about our work as always, only concerned with one customer, one technology and one product family at a time. But it had become very clear by the early 2000s that changes in our industry would eventually catch up with Dresden – and probably sooner rather than later.”

“Then, when we became part of GF, we found ourselves literally overnight in a completely different ball game. It was like going from playing volleyball to playing rugby. You can imagine that it took time and quite a few hard knocks before we adjusted to the rules of the new game,” he recalls.

“Welcome to GLOBALFOUNDRIES Fab 1” was projected on the façade of the Dresden office building in March 2009.

For example, Jens mentioned the steep hiring ramp in 2009/10 that brought plenty of “new blood” from companies like Qimonda, Chartered Semiconductor and from the solar industry. They joined a fairly homogeneous team deeply steeped in the AMD culture. “Although this brought excitement and fresh outlooks, it still was a shock to the system because until then we had been a merry band of AMD brothers and sisters, so to speak, and we now had to cope with change everywhere you looked. For sure, we went through a period of growing pains,” he said.

But that was then, and now GF Dresden is a force to be reckoned with in the worldwide foundry industry. For example, GF’s innovative 22FDX® FD-SOI technology is ramping and is drawing more and more interest for some of the fastest-growing applications in the industry, and so are the 28, 40 and 55/65 nm platforms.

“Today, we have a long-term strategy in place that positions Dresden as a high-mix ‘More than Moore’ foundry fab with a focus on demanding markets such as automotive, security, 5G and AI. With that, we are now becoming part of key European industrial value chains such as automotive,” he said.  Jens noted that the company’s pivot in 2018 dovetailed with the Dresden site’s strategic move away from straightforward scaling towards feature-rich platforms for new markets besides computing and communications.

“We have never seen better alignment between corporate and site strategies, which allows us to stay fully focused on serving the diverse needs of new and old customers and their exciting markets.”

His conclusion: “The future of Dresden looks bright, a ‘season of light’ as Charles Dickens would say. We have successfully redefined the value we bring to our customers and their markets. Our growth potential is real and we have learned, sometimes the hard way, what is expected of us: Strong platforms that make the difference for our customers’ customers, continued innovation, and of course solid execution with focus on quality, cost and the bottom line.”

A Focus on the Customer

If building a sense of shared purpose was the imperative at Fab 1, a world away at GF’s Fab 7 in Singapore keeping customers happy and generating solid financial returns have been the main goals. That’s according to Peter Benyon, Vice President & General Manager of Fab 7. Peter, a 20-year employee split equally between GF and Chartered Semiconductor, was recently named Vice President and General Manager of Fab 8 in Malta, NY, effective July 1.

GF’s Singapore fabs offer a number of mature 200mm and 300mm processes, and soon also will offer GF’s 8SW technology for RF applications, which will be moving there from Fab 10 in East Fishkill, NY. Fab 10 is being sold to ON Semiconductor. (GF’s East Fishkill-based 45RFSOI and silicon photonics technologies will be moved to Malta.)

From the outset, employees in Singapore have had a strong customer focus and a “first-time-right” mentality in the fab that has led to both customer satisfaction and good margins for GF.

GF’s “Opening Day” in Singapore

“We’ve been a foundry from the beginning, and we’ve always had an intense focus on the customer because when it comes to mature technologies, the pricing and other deliverables offered by competing foundries are generally in the same ballpark. Therefore the question on the customer’s mind is always, “Why should I come to you?” Peter said.

“Our answer to that is to focus on their needs by giving priority to their work as required, offering flexibility our competitors usually don’t, and doing what we say we’ll do. As a result, we have built many strong, longstanding and mutually beneficial relationships,” he said.

Foundry Files asked how that mindset might be made to percolate throughout the entire company. “It would be wrong to try to accomplish that by force-fitting a culture onto other GF locations. That has been tried before and it didn’t work. But ensuring customer needs are a priority, always and everywhere, needs to be part of our DNA,” he said.

Peter will take up that challenge in his new role at Fab 8, where his customer focus and proven operational excellence will enhance GF’s ability to serve a rapidly expanding customer base, and will provide a competitive edge over and above the fab’s world-class technologies.

Taking Delight in New Ways to Innovate

“Innovation is what gets our people excited, but scaling isn’t the only kind of innovation. For people who want to do new and unique things, the opportunities are endless. Silicon photonics is an example. Today it’s a small market. However, tomorrow it may be a very large one, and we’re helping to define it,” said Neil Peruffo.

Neil is Vice President & General Manager of Fab 10 in East Fishkill, and has been with IBM and GF for more than 30 years in technology development, characterization and deployment roles.

GF acquires IBM Microelectronics fab in East Fishkill, NY in July 2015

IBM has had a long and illustrious history in semiconductors, with a list of notable accomplishments and individuals too long to mention here. But because the major focus of its semiconductor operations was to support IBM’s mainframe and server businesses, there was concern within the company’s chip unit over a “narrowing corridor of applicability,” as Neil puts it, for its most advanced and costly technology.

He said the concern turned into excitement when the announcement was made in 2015 that GF was acquiring IBM’s semiconductor operations, because it created a sense that new horizons were opening up.

“Even as we were ramping advanced 14nm SOI technology for IBM servers we saw that our fab loading was going down, and although we knew we were core to IBM it was clear that core was requiring less silicon,” he said. “We recognized that our survival depended on expanding into areas such as RF and silicon photonics to reverse the loading trend. So, we went through a pivot of our own to do exactly that even before the GF corporate pivot took place.”

Neil said that after the acquisition many team members felt proud and relieved.  They felt that being part of a pure-play foundry enabled them to continue their careers in the semiconductor industry, while providing new opportunities for growth.

What about now, though, with GF’s pivot away from scaling and toward differentiated and derivative technologies? “When you’re scaling, it’s easy to see what’s next because there’s a roadmap and you basically know what the next steps are going to be. However, on this path of differentiation that GF is taking we have to create our own path forward, which means there’s some uncertainty and we have to think differently about what it means to innovate,” he said.

“What does AI become? What about the IoT? 5G? We have to think beyond the scaling roadmap because there simply is no roadmap to create solutions in these different spaces. For our people, who are curious by nature, who are excited by technology, and who want to do new and unique things, the future holds great opportunities.”

In the next installment in this series, Gregg Bartlett, GF’s Sr. VP of Strategy & Asset Management, tells journalist Dave Lammers about the ups, downs and unexpected twists in the evolution of GF’s corporate strategy over the years.

About Author

By: Martin Mason

GF’s strategy is to deliver highly differentiated, high-added-value solutions for clients in high-growth markets, and a tangible result of that commitment is our 22nm FD-SOI (22FDX®) embedded MRAM non-volatile memory (NVM) technology, which has entered pilot production for several large IoT clients.

The embedded STT-MRAM (spin-transfer torque magnetoresistive RAM) technology, developed in partnership with Everspin Technologies, Inc., is aimed at IoT, general-purpose microcontrollers, automotive, edge AI and other applications where low-power operation and fast, robust, non-volatile code and data storage is a critical requirement.

GF’s eMRAM technology is unique in that it is a robust MRAM solution, having been designed as a high-volume embedded flash (eFlash) replacement. It has passed rigorous real-world production testing, and enables delivery of persistent data retention and endurance at extended temperatures. That is key for microcontroller applications and wireless connected IoT, where embedded memory must retain code and data at high temperatures, including through solder reflow at 260°C during PCB assembly. It also offers erase and (re)write speeds an order of magnitude faster than eFlash (200 nanoseconds vs. 10’s of microseconds), with comparable read speeds, providing a power advantage over eFlash in many applications.

Although it initially makes use of GF’s power-efficient 22FDX process, MRAM is deployed in ‘back-end-of-line’ metallization, which allows for a robust roadmap with planned derivatives on both FDX™ and GF’s FinFET technology. That’s because STT-MRAM as a memory technology allows for process variations which can be used to tune the memory bit cell. Accordingly, we expect to offer two “flavors” of eMRAM: eMRAM-F on 22FDX for code/data storage right now, and eMRAM-S as a working memory to augment SRAM at 1x nodes in the future.

GF is currently running multi-project wafers (MPWs) with 22FDX-based eMRAM-F designs for several clients, and multiple production tape-outs are scheduled in the next three quarters. Custom design services are available from GF and our design partners, and 22FDX eMRAM process design kits are available with macro densities ranging from 4Mb to 32Mb for individual macros. A 48Mb macro is scheduled for release in 4Q19 as well.

Industry is at a Transition Point

What’s driving great interest in alternative embedded NVM technologies at present is the fact that the industry is at a transition point: The 28nm node is possibly the last cost-effective node for eFlash, and the transition to 22nm geometries is making it imperative to find an alternative that is suitable for new and fast-growing low-power applications.

Many new eNVM memory technologies look interesting but aren’t yet production-ready. For example, RRAM (resistive RAM), which stores data by changing the electrical resistance of a dielectric, is the subject of much research and development but its maturity on 2x nm process nodes is limiting its adoption. Likewise adoption for PCM memory is limited by a lack of foundry support below 28nm.

By contrast, eMRAM from GF is an especially compelling and timely solution. Although the technology is complex and has required a significant investment of time and money to develop and deploy, it offers tremendous performance and versatility. In addition to the power benefits gained by combining the power-efficient base FDX silicon-on-insulator process with eMRAM, GF’s FDX process also has industry-leading RF connectivity capabilities, and extensive IP is available from GF. This all enables delivery of a unique high performance, high integration, low power and small size solution, which brings tremendous value to clients.

In fact, GF is on its third generation of MRAM technology with the 22nm eMRAM product, having also produced Everspin’s 256Mb 40nm and 1Gb 28nm standalone MRAM products as part of a joint development effort.

In addition to eMRAM technology, GF also offers clients eFlash and system-in-package flash (SIP Flash) embedded memories, built in a range of technologies from 130nm to 28nm to meet a broad swath of application requirements.

A Successful Strategy

The rollout of 22FDX eMRAM technology truly shows the success of GF’s efforts to intensify investment in areas where we have clear differentiation and where we can add great value for clients.

About Author

Martin Mason

Martin Mason is Sr. Director of Leading-Edge eNVM at GLOBALFOUNDRIES. Before joining the company, he was at Maxim Integrated Products in Executive Director roles for Core Products and Precision Conversion Solutions, and prior to that, he served in product marketing and design/application engineering positions at Atmel, Actel, Concurrent Logic and GEC Plessey Semiconductors. He is a graduate of University of Newcastle upon Tyne in England.

By: Peter A. Rabbeni

AI touches our lives in many different ways, and while some AI-enabled applications are highly visible, like the increasingly popular Amazon Echo and Google Home voice-controlled intelligent digital assistants, others are less obvious. But by no means are they less important.

For example, AI techniques are essential to the successful rollout of 5G wireless communications. 5G is the developing standard for ultra-fast, ultra-high-bandwidth, low-latency wireless communications systems and networks whose capabilities and performance will leapfrog that of existing technologies.

5G-level performance isn’t a luxury; it’s a capability the world critically needs because of the exploding deployment of wirelessly connected devices. A crushing amount of data is poised to overwhelm existing systems, and the amount of data that must be accessed, transmitted, stored and processed is growing fast.

5G Needed for the Upcoming Data Explosion

Every minute, by some estimates, users around the world send 18 million text messages and 187 million emails, watch 4.3 million YouTube videos and make 3.7 million Google search queries. In manufacturing, analysts predict the number of connected devices will double between 2017 and 2020. Overall, by 2021 internet traffic will amount to 3.3 zettabytes per year, with Wi-Fi and mobile devices accounting for 63% of that traffic (a zettabyte is 12 orders of magnitude larger than a gigabyte, or 1021 bytes).

The new 5G networks are needed to handle all of this data. The new networks will roll out in phases, with initial implementations leveraging the existing 4G LTE and unlicensed access infrastructure already in place. However, while these initial Phase 1 systems will support sub-6GHz applications and peak data rates >10GBps, things really begin to get interesting in Phase 2.

In Phase 2, millimeter-wave (mmWave) systems will be deployed enabling applications requiring ultra-low latency, high security, and very high cell edge data rates. (The “edge” refers to the point where a device connects to a network. If a device can do more data processing and storage at the edge – that is, without having to send data back and forth across a network to the cloud or to a data center – then it can respond more quickly and space on the network will be freed up.)

AI and 5G are Perfect Partners

AI functionality is key to edge computing because it provides for more effective control of networks, cells and devices. Without it, many 5G applications that rely on edge computing simply couldn’t be implemented, wouldn’t work well, or would cost too much to deploy.

Take the case of adaptive beamforming, where signals from phased array antennas are combined in ways that increase signal strength in a given direction. It’s important for 5G applications because while spectrum availability in the mmWave frequency range (30GHz – 300GHz) is nearly infinite, signals in these wavelengths are attenuated by atmospheric absorption which limits their usable range to about 300 meters. They also have difficulty penetrating buildings and foliage.

In the past, systems leveraging mmWave frequencies were built accepting these limitations, but that also limited their application. The control of adaptive beam-forming antenna arrays used for mmWave 5G communications is critical to optimizing their operation and performance, and so with the advancement of semiconductors and faster digital signal processing, sensing systems combined with AI can be used to control them. This will lead to dynamically optimized base stations and computing resources which better accommodate changing user needs and environmental conditions. Without AI, this would be much harder to achieve.

Smart Surveillance Cameras

Another way in which AI conserves network resources is its role in the growing use of “smart” surveillance cameras, which make use of diverse semiconductor technologies. More than 120 million IP (internet protocol) cameras were connected to networks globally in 2016, for use in a wide range of applications.

Many of these are so-called “smart” surveillance cameras. (In one notable instance recently, smart surveillance technology enabled police to pick out a wanted man among a crowd of 60,000 concert-goers.)

Without AI to enable edge processing of most of the data generated by a smart camera, though, networks would be overloaded. A single high-definition IP smart camera generates a video stream of 10Mb of data (or 30 frames) per second. Multiply that by the millions of such cameras added in recent years, and the network bandwidth required just for this application would be over a petabyte per second (1015) ─ clearly impractical.

AI to the Rescue at the Edge

Moreover, processing this data in the cloud would be hugely expensive with current technologies. The only real answer is to compute at the edge, using AI techniques for object recognition, gesture detection and classification, and only send minimal metadata over the network.

One might think that the most advanced, leading-edge semiconductor technologies are required to do this, but in fact a number of processes come into play. GF offers the industry’s broadest set of technology solutions for a range of 5G and edge-connected applications, including mmWave front end modules (FEMs), standalone or integrated mmWave transceivers and baseband chips, and high-performance application processors for mobile and networking.

For example, GF’s RF SOI, SiGe and FDX™ FD-SOI offerings are designed to serve applications ranging from sub-6GHz to mmWave frequency bands. RF SOI and SiGe solutions deliver an optimal combination of performance, integration and power efficiency for FEMs with integrated switches, low noise amplifiers and power amplifier applications. FDX offerings are well-suited for the next generation of connected devices such as smart cameras which require ultra-low power technology with intelligence and wireless connectivity built in.

Clients can take advantage of the back gate body-biasing capability of FDX that can be used to dynamically increase performance when needed for image processing, AI/Machine learning, or controlling leakage when a system is in standby. The FDX ecosystem of IP partners includes optimized IP for on-chip  power management, radio sub-systems, low voltage SRAM, instant-on MRAM, eNVM and FPGA blocks for the highly integrated flexible systems-on-chips (SoCs) needed for AI-enabled edge computing.

Connected Intelligence

Traditionally, the industry has viewed networks non-holistically, on a transactional basis and from the separate and distinct viewpoints of computation, storage and data transport.

But if we now think about adding many edge-connected devices with sensor collection capabilities to the network, we begin to see the value of creating networks with smart sensing capabilities, whose data can be collected and become the basis for intelligent and time-based decisions that improve and optimize the services provided by the network.

This is what we mean by Connected Intelligence – the ability to sense, decide and act upon information collected from devices/sensors connected to the network to create the ultimate user experience.

The addition of AI engines to augment this “sense, decide and act” approach to network optimization can create a very powerful framework to best leverage available network assets.

Underneath it all is the realization that AI-enabled 5G mmWave networks will depend on the advancements and innovation in semiconductor technologies. No single technology solution will serve all potential applications. It’s going to require a range of technologies to make these next-generation applications work together seamlessly and maximize their potential.

GF is working closely with clients to understand their needs in this space, and is leading the industry with our portfolio optimized to help meet the demands of AI at the edge.