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What’s next for smarter, more connected electronics manufacturing - Part 2The fast-maturing infrastructure now enabling applications for big data and artificial intelligence means disruptive change not just at individual companies but also in data connections among companies across the microelectronics manufacturing value chain. SEMI checked in with some leading players on the changes they see coming in the next several years for this article series. The trade group is expanding its programming on smart manufacturing to address these industry-wide developments at SEMICON West, July 10-12 in San Francisco.“The ramp of EUV, and the smaller geometries and smaller process margins, will drive an exponential increase in the amount of metrology data to manage,” says Neal Callan, ASML vice president, Silicon Valley. Callan notes that moving to multibeam e-beam inspection will increase data volume from megabytes per second to gigabytes per second and from thousands of data points to millions of data points. “The process is so tight and the margin so small that stochastic variation, or noise, becomes more dominant – at least it’s noise until we can learn to understand and control it. And understanding and controlling this variation will be key to delivering 5nm patterning,” he says.Single-beam e-beam inspection is already driving large increases in data as engineers extend the slow technology to broad, high-speed defect metrology applications by more intelligently instructing the system where to look for problems. Callan says ASML is now using the scanner data on wafer focus, alignment and leveling. The company is also using the computational lithography model from the design to identify the smallest process windows in the pattern that are most likely to see problems. The model then quantifies the number and significance of those instances.“The collection of all this diverse data means that tools will need to be plug-and-play so all tool data is instantly available to all systems and software,” says Doug Suerich, PEER Group product evangelist. “We need tools that can be discovered automatically by the network so it can start slurping up data immediately. The adoption of the Interface A (EDA) standard is accelerating and fabs are starting to ask for it. The proliferation of sensors also needs to self-discover. If you are going to add thousands of new sensors into a facility, you can’t afford a time-consuming integration process.”“We are now seeing that engineers are greedy for more data – if they can get the data, it’s becoming a need-to-have,” adds Tom Ho, BISTel America president. “Getting more data from more sensors, from the sensors on the tool that are not being fully utilized, and from untapped data sources like vibration is another big coming opportunity.” Process complexity drives demand for feed-forward between silos with computational models ASML co-optimizes its scanner process with etch and reticle process steps. Source: ASML In addition to the drive for trace-back of data, the increasing complexity of interrelated processes is also driving demand for feed-forward of data. “Feed-forward is becoming more important,” notes Ho. He points to the example of 3D NAND features, now getting so deep that identifying the layer being measured is a challenge unless the signal at the step before can be recognized. “We need partnerships with our peers to understand how to take advantage of the sensors they use, integrate them with our data, and then feed-forward corrections to the other systems,” concurs Callan. “To drive the best CD uniformity and overlay, we need to co-optimize litho and etch,” agrees Henk Niesing, ASML director of product management. He notes that the company is working with etcher makers to measure the overlay and CD, decompose the finger prints, and then use models to steer automated control that best adjusts both the scanner and the etcher. ASML is also working with Zeiss on co-optimization between the scanner and the reticle to make even higher-order corrections by locally modifying the reticle.These higher-order corrections, applied on each exposed field, drive the need for even more data, and at higher speed but without higher cost, notes Jan Mulkens, ASML senior fellow. These corrections increase demand for computational metrology, which combines various metrology sources with physics and deep learning models trained on real data to predict and control process results in real time. “We’re working on computational metrology to ideally use all the knobs we have in the fab,” he says. So far this effort has largely involved linking data between two companies. More consistent data formats would enable data exchange to be extended to more companies. “The software versions also need to be managed for upgrades so they still match after one party updates the system on its tool,” notes Niesing. Speakers on these issues of smart manufacturing and data handling at SEMICON West include Active Layer Parametrics, Applied Materials, Applied Research Photonics, ASML, Cimetrix, Coventor, ECI Technologies, Edwards Vacuum, Final Phase Systems, GE Digital, Infineon, Jabil, Lam Research, Osaro, Otosense, PEER Group, Rockwell Automation, Rudolph Technologies, Schneider Electric, Seagate, Seimens, Stanford University, TEL, TIBCO Software. See semiconwest.org.What’s next for smarter, more connected electronics manufacturing - Part 1What’s next for smarter, more connected electronics manufacturing - Part 3Paul Doe, SEMI
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What’s next for smarter, more connected electronics manufacturing - Part 1The fast-maturing infrastructure now enabling applications for big data and artificial intelligence means disruptive change not just at individual companies but also in data connections among companies across the microelectronics manufacturing value chain. SEMI expands its smart manufacturing program with a Smart Manufacturing Pavilion with displays and three full days of talks to address these industry-wide developments at SEMICON West, July 10-12 in San Francisco.Autonomous autos’ demand for zero-defect systems and 100 percent traceability back to the manufacturing data for each die is driving a push to traceability across the chip sector. “Far more chips are being used by the automotive sector, and its very different requirements are driving demand for traceability,” says Tom Ho, president of BISTel America. “Our chipmaker customers are looking for traceability solutions and the trend is the same in backend packaging and assembly – automotive applications are driving the sector to traceability.”Traceability is also driven by the growth of systems in a package as fabless chipmakers look to connect back to the packaging companies’ fault analysis labs and die interconnect history to diagnose and fix the cases where known-good die are failing in the system, adds Mike Plisinski, CEO of Rudolph Technologies. Plisinski adds that makers of consumer products like phones that can also see harsh conditions are demanding higher quality and traceability as well. The electronic manufacturing services (EMS) sector also must establish an architecture for traceability to collect critical manufacturing-related data and to interface with OSATs and semiconductor fabs. The reason is that EMS companies are adding traditional OSAT processes such as assembly of products with bare die and complex optics modules requiring clean rooms. “A unified sand-to-smart-phone smart manufacturing roadmap should be established,” says Dan Gamota, vice president of Engineering and Technology Services at Jabil. “We need to identify protocols for manufacturing data communications that can be adopted across the supply chain.”To enable smart manufacturing, vendors need to collaborate on getting their production equipment to interoperate and support factory analytics and data management systems. Source: SEMI One big challenge, of course, is how to format this diverse data so it can be linked and used by various supply chain stakeholders. “Smart data needs to be contextual and it needs data standards across the supply chain so it’s easy to link from the front end to the back end, follow common lot IDs front and back end, and have a way to map streaming data from sensors to a discrete lot ID,” notes Ho. New approaches to metrology, analysis and test that increasingly exploit machine learning on simulations will also be needed to help predict which die and connections that test well now may fail in the future as conditions change.Another issue is how to securely share the needed data across companies without jeopardizing IP. “On the equipment side we collect data across customers on how the tool is running to improve the equipment,” notes Neal Callan, ASML VP Silicon Valley. “Next we need to integrate performance and reliability data that today is not as well shared.”The other big hurdle is how to pay for data sharing. “The challenge is that the final manufacturers reap the benefit of traceability, but since they expect their suppliers to deliver good die, they don’t want to pay more for it,” notes Plisinski. He suggests that over the next two to three years, traceability and predictive fault prevention will become the norm as the automotive sector is compelled to invest in it to assure safety. Meanwhile, fabless companies will face so much complexity in integrating different die from different suppliers in SiP that they will no longer be able to afford to simply use the cheapest supplier, potentially driving a fundamental shift in relations and division of labor among fabless chipmakers, OSATs and fabs. Standards extend across supply chainSEMI member committees are collaborating to build the infrastructure to enable these developments. Standards committees are updating standards for higher bandwidth data exchange and extending semiconductor-like vertical and two-way horizontal equipment communication standards to flow shops to enable assembly players to optimize and trace back results across players. The SMT/PCBA community is integrating its smart manufacturing work into SEMI standards, and the SEMI A1 standard was a key reference document in the development of the Japan Robotics Association’s Equipment Link Protocol.Speakers addressing these issues at SEMICON West include Active Layer Parametrics, Applied Materials, Applied Research Photonics, ASML, Bosch Rexroth, Cimetrix, Coventor, ECI Technologies, Edwards Vacuum, Final Phase Systems, GE Digital, Infineon, Jabil, Lam Research, Osaro, Otosense, PEER Group, Qualcomm, Rockwell Automation, Rudolph Technologies, Schneider Electric, Seagate, Siemens, Stanford University, TEL, TIBCO Software. See semiconwest.org.What’s next for smarter, more connected electronics manufacturing - Part 2What’s next for smarter, more connected electronics manufacturing - Part 3Paula Doe, SEMI
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As artificial intelligence’s (AI) sprawling influence reshapes industries from logistics and healthcare to automotive and manufacturing, Taiwan is poised to leverage its cutting-edge capabilities and rich history in semiconductor manufacturing to stake out a leadership position in AI. Taiwan’s semiconductor manufacturing industry accounts for a major share of the region’s GDP and, with its manufacturing prowess, the region is fertile ground for using AI to optimize and even revolutionize chip manufacturing. In an AI and Semiconductor Smart Manufacturing Forum recently hosted by SEMI Taiwan, experts from Micronix, Advantech, Nvidia and the Ministry of Science and Technology of Taiwan (MOST) shared their insights on how deep learning, data analytics and edge computing will shape the future of semiconductor manufacturing. Here are four key takeaways.1. Monitor, Forecast, and PreventToday, tier 1 foundries use AI tools to combine equipment know-how and manufacturing statistics in managing massive Fault Detection (FD) data, much in the way that a car’s tire-pressure monitoring system helps maintain safe inflation levels and prevent accidents. For example, AI enables the real-time collection and monitoring of massive amounts of processing data, then alerts system administrators of any hardware failures or other manufacturing abnormalities.AI also makes it possible to adopt Run-to-Run (R2R) control to automate manufacturing process adjustments and corrections by providing feedback that can drive higher processing efficiency. In addition, virtual metrology replaces manual sampling inspection for comprehensive quality control, enabling foundries to improve yields, reduce costs, and strengthen their competitive advantage.2. Beyond Automation: Edge Computing The evolution of IoT is giving rise to a paradigm shift in the industry as the recognition grows that smart factories must go beyond automation to focus also on intelligence. All information – from equipment status and manufacturing process statistics to on-site environmental data – needs to be collected through sensors. In highly time-critical scenarios, returning all sensor data to the cloud for processing is time-consuming and impracticable. This is where edge computing’s real-time features and lower cost than cloud computing come into play.How does edge computing work in a smart factory? First, a rich trove of data from various devices is collected and integrated via Manufacturing Execution Systems (MES). Software analysis then produces a real-time factory production status before production data is visualized through a combination of system platforms and human-machine interfaces. In the end, the data is analyzed realtime in the cloud so failures can be predicted and prevented to help increase capacity and reduce costs. The approach is even capable of Bill of Materials (BOM) predictions, allowing better collaboration between upstream and downstream suppliers.3. Deep Learning Accelerates AI Deep learning enables autonomous driving, intelligent voice assistance and many other AI breakthroughs. The heart of deep learning is its ability to automatically process and learn data in various formats such as images, video and text with no human domain knowledge. This increases predictive accuracy and efficiency in processing massive amounts of data. Deep learning also enhances the efficiency of human-machine collaboration.4. Taiwan’s Competitive Niche: Industry 3.5Industry 4.0 is not just about improving production management. It also focuses on integrating supply chains, even among competitive companies. For Industry 4.0 to thrive, rival companies must grow together. The first and third industrial revolutions centered on disruptive technologies like steam engines, transistors and digital, while the second and fourth revolutions homed in on competition among various business models, platforms and industry ecosystems.While Taiwan’s strengths include innovation, short time-to-market, low manufacturing costs, and high supply chain management efficiency, the region still lags advanced countries in basic industry and research capabilities. Squeezed by Chinese supply chains and high-end manufacturers in advanced countries, Taiwan should start by carving out an Industry 3.5 niche for the island’s manufacturers. SEMI will continue to facilitate cross-industry connection, collaboration and innovation to help manufacturers seeking higher production efficiency and lower costs incorporate AI as a core competitive advantage. At SEMICON Taiwan 2018, SEMI will unveil its Smart Manufacturing Journey, an exhibition that gathers leading AI companies such as ABB, Advantech, Nvidia, Sony and UPS to demonstrate a comprehensive roadmap for smart manufacturing technologies and applications. For more information, please visit the SEMICON Taiwan website.Emmy Yi is a marketing specialist at SEMI Taiwan.
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Chip testing is becoming smarter and more complex, creating growing requirements to stream data in real time and ensure it is ready to use for analysis, regardless of the vendor source. Adaptive testing using machine learning to predict die performance in a downstream test can reduce the number of cycles by as much as 40 per cent without compromising test performance, notes Dan Sebban, VP of data analysis, OptimalPlus, who’ll speak on machine learning challenges at SEMICON West’s Test Vision 2020 program. “As devices and their test requirements grow in complexity, the motivation for automating adaptive test greatly increases,” he states, adding that characteristics such as die location on the wafer, defects on neighboring die, condition of the tester, and test values near the specification limits can help predict which die are likely to be good.“The big issue we see is that while everyone likes the idea of machine learning, it remains a black box model, with little visibility into why it makes the decisions it does,” adds Sebban. In addition, a suitable infrastructure to run, deploy and assess a machine learning model in real time is required. “There is still some hesitation to adopt machine learning. It’s a big change of mindset. While building the confidence to use machine learning will take time and experience, using the technology to automate big data analysis with the relevant infrastructure may be our best alternative to reduce test cost.” Systems test and parts-per-billion quality become the ruleSystems test will continue to become more prominent and more complex as chips and packages shrink, affirms Stacy Ajouri, Texas Instruments system integration engineer and Test Vision 2020 event chair. “Even IC makers now need to start doing more systems test.” And as more ICs are used in automotive applications, the distinction between consumer and automotive requirements is blurring, driving demand in other markets for higher precision test with parts-per-billion defectivity requirements.“Intelligent test gets increasingly challenging as devices become more complex and as testing moves from distinguishing good from bad devices to figuring out how to repair and trim marginal devices to make them good,” adds Derek Floyd, Advantest director of business development, this year’s program chair. “We’re highlighting efforts to create the infrastructure the industry needs to manage big data for machine learning with test platforms from different vendors,” says Ajouri, citing work on new standards for streaming data from the testers and labeling critical steps in consistent language to simplify the use of data from different platforms in real time. “I have 10 platforms from multiple vendors, and I need them to mean exactly the same thing by ‘lot’ so I don’t have to sort it out before I can use the data,” she says.Are devices becoming too complicated to test at the required price point?Can testing be economical with up to a million die per wafer, 50 data points per die, a requirement for parts-per-billion accuracy, and the need to identify parts that test good now but that might fail in the future? Organizers of the event invite chipmakers and test suppliers to debate the issue. “The speed of innovation in the semiconductor industry challenges test to keep pace,” notes Floyd. “The product we’re testing is always ahead of the product we have to test it with.”The two-day event features sessions on automotive test; big data and machine learning for adaptive test; handling and interface issues such as over-the-air testing; and a general session covering memory and RF test.Paula Doe, SEMI
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The fast-growing automotive semiconductor market means big change for the IC supply chain. Beyond the obvious demands for reliability and traceability, the sector is moving towards simpler and lower-cost solutions while facing the daunting challenge of automating driving in a complex world. The need for simpler and cheaper automotive intelligence will likely drive acquisitions to build complete platform solutions that are easier to integrate. This demand has already spawned a market for pre-configured test cars to save developers time and money, and is driving LiDAR (Light Detection And RADAR) towards lower-cost, solid state solutions. “The growth of the automotive electronics market provides a great opportunity for the IC supply chain to differentiate on specialty processes and quality for the high-volume automotive business with its long design cycles,” says Scott Jones, principal, strategy, at KPMG, who will speak in the automotive program at SEMICON West. “This differentiation is a chance to reduce chip suppliers’ dependence on scaling volume for the mobile phone world with its short-cycle volatility of winning and losing sockets.” He notes that increasing demand for automotive ICs is also reinvigorating the eight-inch supply chain and spurring opportunity for specialty products such as compound semiconductor devices for power efficiency. Supplying the automotive market also means addressing automotive reliability requirements, which can be 10 times more stringent than for consumer devices. At the same time, the industry must sustain fast-paced development cycles required for the volume and diversity of low-cost IoT devices, manage the segmented supply chain for both those markets, and still spread development costs. Another big challenge for the supply chain will be to automate testing and update vast amounts of embedded software in these automotive devices. “The more complete solution a company can put together, the more the automakers will gravitate to it. They want simplicity,” Jones suggests. Smaller players will need to differentiate with IP and acquire other IP provider to build a broader platform, or be acquired and folded into an all-in-one solution.AutonomouStuff helps accelerate and simplify development of autonomous driving solutionsAutonomouStuff is helping to speed development of these platforms. The company has grown from a sensor distributor into a supplier in the emerging niche of vehicles preconfigured with key interfaces for sensors and controls. These interfaces can then be customized by integrating different components for developers to test their applications. AutonomouStuff offers developers a lineup of vehicle models pre-configured with the interfaces needed to add desired chips, sensors and software to develop their autonomous vehicle systems. Source: AutonomouStuff.“Whether they’re major chipmakers or AI software startups, they don’t have a year to build their own vehicle platforms themselves for developing autonomous vehicle systems,” says Wolfgang Juchmann, VP sales and business development at AutonomouStuff. Juchmann, a SEMICON West speaker, will bring a demonstration vehicle to the show. “In four to six weeks we can prepare a custom test car with selected sensors, enabling users to start testing their computer platforms and software. It’s faster and more cost-effective for us to supply the car with the needed interfaces.” He notes that developers are using some 300 AutonomouStuff vehicles in the field. AutonomouStuff customers are starting to transition from testing on a single car or two to testing on mini-fleets with 50 to 100 vehicles. Beyond sensors and pre-configured vehicles, the next step will be to add more data intelligence services to help with capabilities like tagging the data for training, Juchmann says. AutonomouStuff already offers hardware to support Baidu’s Apollo open-source software stack and data set. The company was recently acquired by the Swedish holding company Hexagon to help support expansion.CMOS silicon LiDAR nears automotive qualificationInnovations in the hyper-competitive LiDAR market, where burgeoning demand is driving the race to develop various types of solid-state devices, may also help reduce the cost of autonomous vehicles. Among the roughly 40 LiDAR suppliers, at least one – Quanergy – is taking advantage of 45nm and 32nm foundry CMOS volume production. The company uses voltage through the semiconductor stack to change the refractive index, controlling the phases of optical beams and the resulting interference patterns of light exiting the chip to quickly steer the laser beam without the need for moving parts, much like the phased array radar its team developed earlier. Solid state LiDAR image with object recognition software. Source: QuanergySo far, most of the small LiDAR units have shipped to the security, industrial automation, drone, robots and 3D mapping markets. However, Quanergy CEO Louay Eldada, another SEMICON speaker, says the company is also winning automotive designs and expects automotive shipments to take off early next year, once automotive certification testing is completed. “We can get design wins because standard CMOS production at TSMC makes us a known entity,” says Eldada. To prevent component misalignment, the company produces its own specialized packaging to secure the laser, phase control ASIC, optical phased-array emitter, detector array, and receiver readout ASIC at its plant in Silicon Valley or the facility of its automotive partner Sensata. Through its software business, Quanergy offers an artificial intelligence (AI) perception program for object recognition and LiDAR tracking. The solution uses the people-tracker software the company acquired from Raytheon.SEMICON West this year expands to three full days of automotive electronics programming and features a Smart Transportation Pavilion. Other companies with experts who will speak as part of the program include XPT/NIO, Infineon, McKinsey, Voyage, GM Cruise, Bosch, Deepen AI, Airbus A3, Nvidia, Excelfore, Byton, Macronix, SK Hynix, SAP, Xilinx, Achronics, California Fuel Cell Partnership, Velodyne, Lam Research, KLA-Tencor, SCREEN, Rockwell, Versum Materials, TechSearch International, Entegris, ASE, Amazon, Continental and Wind River. www.semiconwest.orgPaul Doe, SEMI
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The U.S. Trade Representative (USTR), based on findings from its Section 301 investigation into China's trade practices, today announced a 25 percent tariff on $34 billion in Chinese goods including many products in the semiconductor supply chain.Products such as test and inspection equipment and spare parts that enter the U.S. from China will be subject to this tariff, which enters into force on July 6, 2018. About 80 percent of the semiconductor products originally proposed remain on the final list of tariffs.USTR also has proposed tariffs on more than $16 billion worth of goods including chemicals as well as machines and spare parts that are used to manufacture semiconductor devices, wafers, flat panel displays, and masks, all of which would squarely strike the semiconductor industry. This new proposed list includes products identified by the U.S. government that have particularly benefited from Chinese industrial policies such as “Made in China 2025.” SEMI is set to voice its opposition to these tariffs with written comments and at an upcoming public hearing.Over the past month, SEMI has submitted written comments and offered testimony on the damaging impact that tariffs would have on the U.S. semiconductor industry. While SEMI strongly supports efforts to better protect valuable intellectual property, we believe that these tariffs will do nothing to address U.S. concerns over China’s trade practices. Instead, the tariffs will harm companies in the semiconductor supply chain by increasing business costs, introducing uncertainty and stifling innovation.SEMI will continue to engage with lawmakers as the $34 billion in tariffs take effect and the proposed $16 billion in duties remain under consideration. We encourage members to review this list and determine the level, if any, of impact. If you have questions or concerns, please reach out to Jay Chittooran, Public Policy Manager at SEMI, at [email protected].
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The Scaling Technologies TechXPOT at this year’s SEMICON West (Scaling Every Which Way! – Thursday, July 12, 2:00pm-4:00pm) will explore traditional scaling as the industry marches toward 3nm and beyond, as well as technologies that enable 3D architectures, die stacking, and interconnect scaling. The session will also provide an update on how the various players (foundry, IDM, fabless, and application developers) are jockeying for innovation leadership. As a prelude to the event, SEMI asked Priya Mukundhan, director, Technology Development and Applications, at Rudolph Technologies, and a speaker at the TechXPOT, to provide her insights into challenges associated with metrology and inspection.For a full list of speakers and program agenda, visit http://www.semiconwest.org/programs-catalog/scaling-every-which-way. Priya Mukundhan, director, Technology Development and Applications, Rudolph TechnologiesSEMI: What are the key challenges that need to be addressed to provide the kind of metrology and inspection solutions that will be needed by the industry as scaling – in all its forms (e.g., traditional, 3D ICs, interconnect, and different transistor architectures) – is pursued at 5nm and then at 3nm? Priya Mukundhan: With respect to metrology needed to scale FinFETs, the following will be key: Gate critical dimension (CD) at the fin sidewall, gate height, gate profile Fin CD, height and profile Dopant profiles Stress measurement in the fin Composition in thin film and interface These challenges are currently being handled using in-line CD solutions, CD scanning electron microscopy (CD-SEM) and CD atomic force microscopy (CD-AFM), along with optical critical dimension (OCD) measurements. There is no single technology that can take all of these measurements, and determining the right solution is application-dependent[1].Issues associated with inspection and scaling include the following: Bright-field inspection lacks the sensitivity to detect defects smaller than those found at the 20nm node Detecting defects that are 5nm or smaller is achieved using electron beam inspection tools, but these single-electron beam inspection systems are prohibitively slow and cannot meet the high-volume manufacturing (HVM) requirements for defect inspection Buried defects Void detection in 3D SiP structures, front and backside inspection Sidewall crack detection in packaging SEMI: Can you provide a summary of the R D roadmap for metrology/inspection tools that you see emerging in order to get to 3nm? PM: Hybrid metrology is currently in use, especially for CD metrology. To support the development efforts, techniques that provide complementary information as well as those that eliminate uncertainties will be required. Researchers at imec[2] have started exploring technology combinations to gain insight into how new structures function. Some of the findings in imec’s study include the following: The combination of transmission electron microscopy (TEM) and scanning probe microscopy (SPM) provides a unique approach of imaging combined with a functional analysis capability In situ SPM could potentially determine composition (SIMS) as well as functional properties (electrical) Fast Fourier transform scanning spreading resistance microscopy (FFT-SSRM) is a novel technique that measures carrier profiles in semiconductors. This overcomes the current SSRM limitations of signal distortions due to parasitic resistances while measuring on small volumes such as FinFET and nanowires Multi-electron beam inspection can be used for HVM for sensitivity to smaller SEMI: How will metrology and inspection be impacted beyond 3nm? What kinds of tools will be needed by that point in time?PM: There are several different transistor options that have been identified by leading edge wafer fabs and consortia looking beyond the 5nm node roadmap[3,4]. Some of the options on the table include the following: 1) Extension of the current FinFET in the form of gate-all-around FET2) Creating them with new materials by adding ferroelectrics (e.g., negative capacitance FET, or NC-FET) 3) Complementary FET4) Vertical nanowires and nanosheet FETsThese possibilities bring new challenges and require characterization at the material level. Also, the industry as a whole will have to redefine what it means to do composition at the nanometer level. This could be the beginning of a trend towards array-based metrology, i.e., measurements on an array of devices to gather statistically significant data[2].Regarding metrology needs at 3nm, it is too early to determine what kind of tools would be needed only for R D and how many of them would need to be extended to high-volume manufacturing (HVM). From an inspection perspective, there will be a continued migration towards computer aided design (CAD)-based inspection, as well as having the ability to deal with large image data sets (petabyte, big data). Furthermore, inspection algorithms should be improved, along with better staging for better image stitching.References Bunday, E. Solecky, A. Vaid, A. F. Bello, X. Dai, “Metrology capabilities and needs for 7nm and 5nm logic nodes,” Proc. Of SPIE, Vol. 10145, 101450G, pp. 1-41, 2017. Imec roadmap and imec magazine. Intel roadmap. https://semiengineering.com/transistor-options-beyond-3nm/ Debra Vogler, SEMI
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The Advanced Lithography TechXPOT at this year’s SEMICON West will explore progress in extreme ultraviolet lithography (EUVL), its economic viability for high-volume manufacturing (HVM) and other lithography solutions that will address the march to 5nm and onward to 3nm.As a prelude to the event, SEMI asked Mary Ann Hockey, director for Advanced Emerging Lithography at Brewer Science Inc., and a speaker at the TechXPOT, for insights into the status of directed self-assembly (DSA) as it applies to the industry’s march to patterning for the 3nm node and beyond. For a full list of speakers and program agenda, visit http://www.semiconwest.org/programs-catalog/lithography-5nm-and-below.Mary Ann Hockey, director for Advanced Emerging Lithography at Brewer Science Inc.SEMI: What is the current status of materials development for DSA?Hockey: We are currently working with strategic customers to implement high-quality DSA chemical material solutions. We are both addressing near-term implementation of standard PS-b-PMMA block copolymers (28-30nm Lo) by leveraging our strategic partnership with Arkema, France, and building a library of high-chi block copolymers for long-term device requirements (Figure 1). SEMI: How do those developments prepare the technology for 5nm, 3nm or beyond?Hockey: We have engaged the strategy of engineering a library of novel high-chi block copolymer (BCP) platforms for next-generation DSA technology requirements of 3-5nm devices. One key objective is a global focus on easing implementation into a manufacturing environment. This objective requires large process windows for guided alignment (accommodating pitch and guide size target variability), minimizing BCP microphase anneal times (short anneal time supports high throughput), and streamlining the total number of process steps required for volume production (Figure 2).SEMI: How will industry’s use of DSA be intertwined with immersion lithography?Hockey: We envision immersion lithography as the foundation enabler with strategic use of optical lithography for generating consistent critical dimension (CD) sizes of DSA guides/templates for low cost of ownership.SEMI: What about the combination of DSA and extreme ultraviolet lithography (EUVL) to fabricate devices at 5nm, 3nm, and beyond?Hockey: EUVL and DSA can potentially work in harmony to support next-generation device technology. DSA can be made with the capability of lithography rectification or enhancing EUVL photoresist sidewalls and targeting low line-edge roughness and line-width roughness (LER/LWR) values.Debra Vogler, SEMI
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With chipmakers looking toward 5nm manufacturing, it’s clear that traditional scaling is not dead but continuing in combination with other technologies. The industry sees scaling enabled by 3D architectures such as die stacking and the stacking of very small geometry wafers. Interconnect scaling also comes into play. This year’s Scaling Technologies TechXPOT at SEMICON West (Scaling Every Which Way! – Thursday, July 12, 2:00PM-4:00PM) will provide an update on the evolution of scaling and describe how the various players (foundry, IDM, fabless, and application developers) are jockeying for innovation leadership. As a prelude to the event, SEMI asked speakers to provide insights on important scaling trends. For a full list of speakers and program agenda, visit http://www.semiconwest.org/programs-catalog/scaling-every-which-way.Challenges for gate-all-around (GAA) and FinFET devicesDiederik Verkest, imec Distinguished Member of Technical Staff, Semiconductor Technology and Systems“During the processing of GAA devices, there are ‘non-line-of-sight’ hidden features that are difficult to control and characterize and may also lead to new defect mechanisms that would impact yield, and possibly product reliability.”Common performance boosters for gate-all-around (GAA) FETs and FinFETs include lower access resistance, lower parasitic capacitance, and stress. “However, one specific performance booster that only applies to GAA is the reduction of the spacing between the vertical wires or sheets,” says Diederik Verkest, imec distinguished member of technical staff, Semiconductor Technology and Systems. “This reduces parasitic capacitance without affecting drive current and hence benefits both performance and power.” He further notes that imec demonstrated the first stacked gate-all-around (GAA) devices in scaled nodes. “In fact, we are the only ones that published working circuits – ring oscillators in a scaled node using industry-standard processes – in our case replacement metal gate (RMG), and embedded in situ doped source/drain (S/D) epitaxy.”“There are two elements of the stacked GAA architecture that need to be addressed,” says Verkest. “The first is that this architecture uses epitaxially-grown layers of Si and SiGe to define the device channel. The use of grown materials for the channel and the lattice mismatch between the two materials represent a departure from the traditional fabrication of CMOS devices, so the industry needs to develop and gain confidence in novel metrology that allows for good control of the layers and also proves their low defectivity.” The second aspect is the three-dimensional nature of the GAA devices. “During the processing of these devices, we have ‘non-line-of-sight’ hidden features that are difficult to control and characterize and may also lead to new defect mechanisms that would impact yield, and possibly product reliability.”Huiming Bu, Director, Advanced Logic/Memory Research - Integration and Device, IBM Research, Semiconductor Group"A new device architecture beyond FinFET is required to provide a full technology node scaling benefit (i.e., density, power and performance) at 5nm and 3nm.”Huiming Bu, director, Advanced Logic/Memory Research - Integration and Device, IBM Research, Semiconductor Group, says that naming of technology nodes has been used extensively for marketing strategies in “foundry land,” but the designations have lost much of their meaning as technology scaling differentiators. “That said, when it comes to technology innovation and value proposition, IBM, in conjunction with Samsung and GLOBALFOUNDRIES, has developed the GAA NanoSheet transistor for 5nm to provide a full technology node scaling benefit in density, power and performance,” says Bu (Figure 1). The key parameters for intrinsic device optimization when scaling to the 3nm node, explains Bu, are the NanoSheet width for better electrostatic characteristics, and the number of sheets for increased current density. Also necessary are strain engineering for carrier transport enhancement, and interconnect innovations for parasitic RC reduction. “Beyond that, the industry needs to look into something different, something more disruptive.”Figure 1. TEM cross section of stacked NanoSheet transistors. SOURCE: IBM Research Materials challengesMaterials challenges are also a concern as the industry moves to 5nm and below. “We see increasing complexity in the material systems that are being used,” explains Verkest. One example he cites in scaled FinFET or GAA technologies is the use of two to three layers of different materials – typically metals such as TiN – to which small amounts of other elements are added to set device characteristics such as the threshold voltage. “At the same time, the requirements for the thickness of these materials, driven by gate dimensions for example, or the distance between the wires, are increasingly challenging.” Other examples of materials challenges are the use of two to three different types of insulators in the middle-of-the-line, each with different etch contrasts. “We use novel materials such as carbon containing oxides or oxynitrides that have lower dielectric constants in order to boost the performance of circuits,” he says, noting that the materials list “is quite long.”Several critical dimensions in transistors at advanced technology nodes have already reached a few monolayers of atoms, fueling expectations for innovation at the material level for transistor scaling, Bu notes. “The other argument is that there is a growing gap between computing demand and the slowdown of technology advancement driven by conventional scaling,” says Bu. One trend that addresses this gap is integrating more computing functions that make the technology solution more modular, which naturally leads to the incorporation of more materials for more applications. Bu cautions, however, that introducing new materials in semiconductor technology has never been easy. “It takes many years of R D to reach this implementation point, if it ever happens. So, do we need new materials when the industry moves to 5nm and 3nm? Yes, though I expect new material implementation to be a lot faster in interconnect and packaging at these nodes rather than intrinsic to the transistor.” Challenges in developing atomic-level processesThere will be challenges in developing atomic-level processes used in scaling, such as atomic layer depositions (ALD) and atomic layer etches, notes Verkest. “These classes of processes are both required to handle the scaled dimensions at the 5nm and 3nm nodes, and also the 3D nature of the scaled technologies – and here we are talking about logic and memories,” Verkest says. “With respect to depositions, we would need to develop thermal ALD processes (not plasma-based) that enable accurate and conformal depositions in non-line-of-sight structures.” Adhesion and wetting, smoothness, and throughput would also need to be addressed. “Longer term, these processes need to facilitate selectivity and self-alignment to address gap-fill challenges in highly scaled structures,” he says. Other concerns he notes with respect to atomic layer etches are selectivity to various materials, and fidelity requirements that increase the requirements for metrology accuracy. “Throughput is also a concern.”Bu believes that a new device architecture beyond FinFET is required to provide a full technology node scaling benefit (i.e., density, power and performance) at 5nm and 3nm. “Beyond 3nm, we may need to continue the transistor scaling in the vertical direction and start to stack them together,” Bu says. He also cites the need for parasitic R/C reduction in the interconnect to take advantage of the intrinsic transistor benefit at the circuit and chip levels. “We see a lot of opportunity in atomic-level processes, especially in atomic layer etch and selective material deposition, to address these challenges in the transistor and the interconnect.” Debra Vogler, SEMI
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The fast-maturing hardware and software that are enabling practical applications of equipment intelligence and machine learning mean disruptive change for microelectronics manufacturing. But first comes the basic work of building the basic infrastructure, figuring out IP separation, and learning to solve physical problems in the digital world. Just how much can the semiconductor industry leverage industrial IoT practices from other industries? Common wisdom may be that industrial software solutions aren’t well suited to the IC sector’s complex needs. But GE Digital enterprise account executive Luke Smaul, currently working with Intel, argues that semiconductor fabs and toolmakers are dealing with similar issues as GE did when it first started working with Delta Airlines to monitor the GE engines on Delta planes. Smaul will speak at SEMICON West about GE’s work with Intel over the past few years and, in particular, how its solution for cloud security and IP separation can work for ICs. “GE learned to provide IP security and separation in the aviation space with its suppliers, which moved us all up the value chain, providing a big engine for growth,” says Smaul, who started his career as an IC engineer. “GE Aviation saw a 25 percent increase in issue detection rates by leveraging the same common platform. We’ve shown that we can protect Intel intellectual property in its own cloud space and control who can access what.” A toolmaker can access only particular fab data as needed for analysis, and then can reveal only the output from the analysis and a subset of supporting data. “IP separation has to happen, and it will unlock huge added value,” Smaul says. GE’s Predix solution aims to supply an easy-to-use, plug-and-play system for analytics to enable a yield engineer without a deep data background to select a supported sensor, a gateway to connect automatically to the cloud, and an analytics application to test a hypothesis of how the collected data relates to yield. “This empowers the yield engineer to use and unlock information for a quick improvement, even for simple things such as looking at the impact of degradation of fan performance over time on yield,” says Smaul. “Though the scope may be small, the impact on yield in aggregate, and when scaled, is large.” “There needs to be much more collaboration across the industry to make this work, and to share best practices,” says Smaul. “Just as GE moved from selling gas turbines to selling power-as-a-service, vendors of other big, expensive assets like IC equipment will likely change their business model from selling tools towards selling yield-as-a-service. This will simplify life for the fab while bringing the toolmaker more opportunity to sell improved capabilities on existing tools.” More human intelligence makes AI smarter Applying AI neural network approaches such as deep learning to predict outcomes from digital models is enabling disruptive advances in speech and image recognition, but applying it to complex IC manufacturing problems such as predictive maintenance has been a challenge. These neural networks require massive amounts of data to train, and the IC sector doesn’t really have big data, just a lot of little data clusters due to the dynamics and context richness of processes. This data is difficult to combine for analysis. In addition, the neural network provides only an answer but can’t explain why, notes Michael Armacost, managing director of advanced service engineering at Applied Materials. “We’ve learned that it works better if we do not ignore what we know already, but rather incorporate expert knowledge in a structured way to help us focus on the key features and the key data,” says Armacost, who will also speak in the program. This includes choosing the most important steps to include in the model, identifying the limited data to collect and how to filter the data for outliers, and then selecting the final parameters and features, adjusting the limits, and making adjustments as results drift change over time. The less data needed, the better for the complicated issue of IP protection as well. The big gains from these new analysis approaches will likely require data from more than one company and supporting security for remote connectivity. “Some end users are attempting to do the AI all themselves, but in the long term there will need to be collaboration across companies,” says James Moyne, University of Michigan professor and consultant to Applied Materials, another speaker. Collaboration will need to balance the value of the solution against the risk of compromising IP. “The low-hanging fruit are applications such as predictive maintenance in areas that do not involve high-priority IP. Another approach will be to limit the amount of shared data needed – to first build the model on a wide range of data, but then to use only a very small amount of data to operate the models.” Ready-made models could speed the process Coventor’s semiconductor process models are finding initial applications in R D whereby companies use the simulation to understand the effect of process variation on their complex designs. Instead of running dozens of actual wafers to optimize semiconductor processes, users can instead quickly simulate the results of complex process interactions on their design. Going forward, the process models could find a wide range of applications, from accelerating stabilization of new processes in the fab to enabling real-time co-optimized control across previously independent unit steps to improve wafer uniformity. “This improved uniformity across wafers and equipment could potentially reduce the need for costly physical silicon validation,” suggests Joseph Ervin, Coventor director, semiconductor process and integration, another SEMICON speaker. “Making use of in-situ metrology for real-time control also demands a digital model to process and analyze the collected data for quick response. This area has tremendous potential for improving semiconductor process control.” SEMICON West features a Smart Manufacturing Pavilion with displays and three full days of speakers on building the infrastructure needed to enable disruptive artificial intelligence in the microelectronics sector. www.semiconwest.org The SEMICON West Smart Manufacturing Pavilion features interactive Touch Liquid Crystal Displays (TLCD) and working production equipment on the floor from Bosch Rexroth, Cimetrix, Rudolph Technologies, Inficon/Final Phase Systems, OMRON, DISCO and Edwards Vacuum. For information on the SEMI Smart Manufacturing Initiative and how to get involved, please click here. Paula Doe, SEMI
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