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By Lung Chu, President of SEMI China

Lung250As China embarks on the Made in China 2025 plan with electronics and semiconductor technology as one of the Top 10 focus areas, China’s semiconductor industry has an unprecedented growth opportunity.  However, besides the huge investment required, China IC industry is faced with strong competition in terms of technology, products, talent, and supply chain access from many leading global layers in an increasingly interconnected world and a highly global semiconductor market.

To be successful, it is critical that China’s semiconductor industry speed up its integration into the global industry supply chain. The goal is to achieve sustainable growth through “win-win” collaboration with global partners and leveraging industry platforms to become a significant player and partner in the international semiconductor manufacturing industry ecosystem.

China semiconductor industry growth

In recent years, many new 12-inch fab projects have been announced, started construction, or in ramp-up stage in China, including UMC in Xiamen, PSC in Hefei, TSMC in Nanjing, YMTC in Wuhan and Nanjing, as well as GLOBALFOUNDRIES in Chengdu.  Many China-based foundries are adding 12-inch capacity including SMIC fabs in Shanghai, Beijing and Shenzhen, and HLMC in Shanghai area. The production capacity of these ~20 new fabs is expected to come online in the next three to five years.

SEMI has seen active interest in several local cities in attracting global and China-based companies to set up semiconductor fabrication facilities.  The strong trend for expansion and investment shows no signs of slowdown in China. The current investment fever in semiconductors in China is a balancing act ─ it will lead both to the development of a regional industry supply chain and the demand for capital investment in China. However, as with any expansion bubble, new production capacity in some mature nodes might create overcapacity and raises questions of sustainability paired with the severe shortage of skilled workers/engineers and uncertainty of future fund availability for continuing operations and investment.

Rise of China

China’s expansion in semiconductor manufacturing should be viewed through a global context.  SEMI advocates for free trade and open markets, international cooperation for intellectual property (IP) rights protection, industry Standards, and environmental protection. SEMI promotes the global electronics manufacturing supply chain and works to positively influence the growth and prosperity of its members.

In 2016, before stepping down, the U.S. Obama administration delivered a report from the Council of Advisors on Science and Technology. Part of the report addressed the rise of China’s semiconductor industry and recommended the United States should improve its environment for development of the semiconductor and high-tech industry and continue to invest in advanced technologies.

Each country will evaluate their own course as the China market expands. However, the rise of the semiconductor industry in China need not be viewed simply as a threat to the world; instead, it is a significant growth driver and business opportunity for global suppliers.  IC chips top the list of all Chinese bulk imports in terms of dollar value. China desires to develop its IC chip industry to better fulfill its inherent demand. China currently has low market share and limited technical capability in four major areas identified in the China National IC Development Guideline: IC design, manufacturing, package/testing, and equipment/material.

China is clear about its intentions with regard to growing its own semiconductor supply chain. In the short term, heavy dependency on foreign suppliers (especially equipment and material) is inevitable.  Going forward, cooperation with foreign semiconductor suppliers/partners with an open-minded and “win-win” attitude is an imperative strategy in solving the development bottleneck issues concerning equipment/materials and other key areas in China’s semiconductor industry.

SEMI China focuses on member value

China is the world’s largest manufacturing base for electronics products, as well as the world’s largest market for demand of IC chips. Now, as China’s semiconductor industry experiences a transformation in development, SEMI China is working to provide more value to its local and global members as the industry is rapidly changing. SEMI China promotes Chinese enterprises for industry growth and prosperity, and helps outstanding local companies advance in the international market. SEMI China is also using its global, specialized, and localized industry association platform to promote the development of the semiconductor industry in China.

SEMI China has 11 industry committees and is committed to SEMI global values and the China region. All the SEMI China committees have the strong connections needed to communicate and collaborate not only with China’s semiconductor industry, but with the global ecosystem.

SEMI, the global trade association that advances the growth and prosperity of electronics manufacturing, was the world’s first semiconductor industry group, established in 1970. It has witnessed the flourishing development of the semiconductor industry over the last 47 years and continues to be devoted to promoting the healthy development of the industry. SEMI is keeping pace with the industry and offering specialized and global platform services to the entire industry ecosystem. In the last two years, SEMI became a strategic partner with both FlexTech Alliance and the MEMS & Sensors Industry Group (MSIG). In the future, SEMI is also providing association services for the Fab Owner Association (FOA) to continue expanding collaboration along the electronics manufacturing supply chain. The intent is to include a wider span of the interdependent electronics manufacturing supply chain and the key adjacent opportunities that drive global growth opportunities.

SEMICON China is an industry event platform organized in partnership with major chip manufacturers, packaging and testing companies in China, and suppliers of equipment and materials worldwide. The world’s leaders come to discuss global industry trends, cutting-edge technologies and market opportunities on the same stage, as well as the development of global and Chinese semiconductor industries. This year, the importance of SEMICON China was validated ─ with over 69,000 attendees and a record number of exhibitors ─ the largest SEMICON show ever.

Global competition in semiconductor manufacturing has long been a part of the environment with growth starting in the U.S. and spreading to Europe, Japan, Korea, Taiwan, Southeast Asia, and China. Global competition has resulted in new innovations and a global march to the demanding cadence of Moore’s Law. Compared to other countries, China’s semiconductor industry is relatively weak and the barriers to entry for leading-node production remain challenging. Despite this, China is moving forward ─ with a focus to increase domestic semiconductor chip demand. The Chinese M&A wave is another growth driver for the industry. I hope that going forward we can all embrace the industry’s growth, and not fear China’s advancement.

 

WIN Semiconductors Corp (TPEx:3105), the world’’s largest pure-play compound semiconductor foundry, has completed phase 2 expansion at its newest wafer fab, Fab C. This operation is now fitted with clean rooms, efficient process lines and advanced equipment for GaAs MMIC production, epitaxial growth of compound semiconductors, as well as fabrication and test of optical devices. Continued build-out of the new manufacturing facility further validates the pure-play foundry model in the compound semiconductor industry.

Serving customers in mobile PA, WiFi, wireless infrastructure and optical markets, WIN Semiconductors provides a broad portfolio of Hetero-junction Bipolar Transistor (HBT), Pseudomorphic High Electron Mobility Transistor (pHEMT), integrated BiHEMT technology solutions and optical devices. WIN Semiconductors’ manufacturing services can support most any application from 50MHz to 150GHz and through light-wave.

“In response to increasing demand across all market segments, we continue to add manufacturing capacity at our third wafer fab located in Guishan, Toayuan City, Taiwan. Known as Fab C, the facility now supports mass production of a wide range of compound semiconductor technologies. When fully built out, the 706,000ft2 facility will more than double our capacity,” said Kyle Chen, Senior Vice President and Chief Operating Officer of WIN Semiconductors.

Win Semi Fb C PR image

IC Insights will release its May Update to the 2017 McClean Report later this month.  This Update includes a discussion of the 1Q17 semiconductor industry market results, an update of the capital spending forecast by company, a review of the IC market by electronic system type, and a look at the top-25 1Q17 semiconductor suppliers (the top-10 1Q17 semiconductor suppliers are covered in this research bulletin).

The top-10 worldwide semiconductor (IC and O S D—optoelectronic, sensor, and discrete) sales ranking for 1Q17 is shown in Figure 1.  It includes four suppliers headquartered in the U.S., two in Europe, two in South Korea, and one each in Singapore and Japan.  In total, the top-10 semiconductor suppliers represented 56% of the 1Q17 worldwide semiconductor market of $99.6 billion (2Q17 is forecast to be the first ever quarterly semiconductor market to exceed $100 billion).

Figure 1

Figure 1

Intel held a slim 4% lead over Samsung for the number one position in 1Q17.  However, as reported in an earlier IC Insights’ Research Bulletin, Samsung is on pace to displace Intel as the world’s largest semiconductor supplier in 2Q17. Memory giants SK Hynix and Micron made the biggest moves in the 1Q17 ranking as compared to the full-year 2016 ranking.  Spurred by the recent surge in the DRAM and NAND flash markets, each company moved up two spots in the top-10 ranking with SK Hynix now occupying the third position and Micron moving up to fourth.

There was one new entrant into the top-10 ranking in 1Q17—Germany-headquartered Infineon.  The company’s 1Q17/1Q16 sales increase was 6%.  Infineon replaced fabless supplier MediaTek, whose 1Q17/1Q16 sales were up by 7% to $1.8 billion but the company suffered a sequential 1Q17/4Q16 sales decline of 17%.  Half of the top-10 companies had sales of at least $4.0 billion in 1Q17.  As shown, it took $1.9 billion in quarterly sales just to make it into the 1Q17 top-10 semiconductor supplier list.

As would be expected, given the possible acquisitions and mergers that could/will occur this year (e.g., Qualcomm/NXP), as well as any new ones that may develop, the top-10 semiconductor ranking is likely to undergo some significant changes over the next few years as the semiconductor industry continues along its path to maturity.

After nearly a quarter of a century, the semiconductor industry could see a new #1 supplier in 2Q17. If memory market prices continue to hold or increase through 2Q17 and the balance of this year, Samsung could charge into the top spot and displace Intel, which has held the #1 ranking since 1993. Using the mid range sales guidance set by Intel for 2Q17, and a modest, yet typical, 2Q sales increase of 7.5% for Samsung, the South Korean supplier would unseat Intel as the world’s leading semiconductor supplier in 2Q17 (Figure 1).  If achieved, this would mark a milestone achievement not only for Samsung, specifically, but for all other competing semiconductor producers who have tried for years to supplant Intel as the world’s largest supplier.  In 1Q16, Intel’s sales were 40% greater than Samsung’s, but in just over a year’s time, that lead may be erased and Intel may find itself trailing in quarterly sales.

samsung 1

Samsung’s big increase in sales has been driven by an amazing rise in DRAM and NAND flash average selling prices (Figure 2).  IC Insights expects that the tremendous gains in DRAM and NAND flash pricing experienced through 2016 and into the first quarter of 2017 will begin to cool in the second half of the year, but there remains solid upside potential to IC Insights’ current forecast of 39% growth for the 2017 DRAM market and 25% growth in the NAND flash market.

samsung 2

As shown in Figure 3, Intel has been locked in as the world’s top semiconductor manufacturer since 1993 when it introduced its x486 processor and soon thereafter, its revolutionary Pentium processor, which sent sales of personal computers soaring to new heights.

samsung 3

Over the past 24 years, some companies have narrowed the sales gap between themselves and Intel, but never have they surpassed the MPU giant.  If memory prices don’t tank in the second half of this year, it’s quite possible that Samsung could displace Intel in full-year semiconductor sales results as well.  Presently, both companies are headed for about $60.0 billion in 2017 semiconductor sales.

Strong growth in MCUs for IoT applications and suppliers jockeying for marketshare in this IC segment have resulted in several major acquisitions that changed the pecking order of MCU leaders in 2016, according to data released in IC Insights’ April Update to The McClean Report, which was released earlier this month. Figure 1 ranks the largest MCU suppliers in 2016 by dollar-sales volume.  Among the top MCU suppliers shown, NXP, Microchip, and Cypress Semiconductor moved up in the sales ranking during 2016 with strong increases in revenues, which were driven by acquisitions of IC companies that sold microcontrollers. Meanwhile, those suppliers not making significant acquisitions in microcontrollers posted low-single digit percentage increases or declines in MCU sales in 2016.

Figure 1

Figure 1

Although overall growth in microcontrollers has wobbled and stalled in the past couple years, MCUs remain at the epicenter of tremendous growth in the Internet of Things, automotive, robotics, embedded applications and other emerging systems.   Major MCU suppliers have been improving their portfolios to address many of these key markets.  Part of that improvement process has included merging and acquiring competitors in order to gain a quick foothold into these developing markets.

In 2016, NXP in the Netherlands overtook Renesas Electronics in Japan as the world’s largest microcontroller supplier with MCU revenues climbing 116% following its $11.6 billion purchase of U.S.-based Freescale Semiconductor in December 2015.  Prior to its acquisition, Freescale was ranked second in MCUs and was catching up with Renesas in microcontroller sales with only $210 million separating the two companies in 2015 versus about a $1 billion gap in 2014.  Renesas suffered a 19% drop in MCU dollar sales in 2015 (largely due to the weak yen exchange rate in that year but also because of the continued fallout from Japan’s troubled economy).  In 2016, Renesas’ fall in MCU sales eased, dropping 4% to nearly $2.5 billion, or about 16% of the total microcontroller market.  In 2011, Renesas’ MCU marketshare was 33% of worldwide microcontroller sales.

The Freescale acquisition moved NXP from sixth in the 2015 MCU ranking to the top spot in 2016 with a marketshare of 19% ($2.9 billion).  About three-quarters of NXP’s 2015 microcontroller sales were 8-bit and 16-bit MCUs used in smartcards.  After Freescale’s business was merged into NXP, smartcard MCUs accounted for a little over one-quarter of the company’s total microcontroller sales in 2016. MCUs developed and introduced by Freescale are aimed at a wide range of embedded control applications, including significant amounts in automotive systems.  NXP and Freescale both have developed extensive 32-bit MCUs with Cortex-M CPU design cores licensed from ARM in the U.K.

U.S.-based Microchip Technology climbed from fifth in the 2015 MCU ranking to third in 2016 with sales increasing 50% to $2.0 billion following its $3.4 billion acquisition of Atmel in 2Q16.  U.S.-based Atmel was ranked ninth in MCU sales in 2015 ($808 million).  Prior to buying Atmel, Microchip had been the only major MCU supplier not licensing ARM CPU technology.  For about 10-years, Microchip has developed and sold 32-bit MCUs, based on a RISC-processor architecture developed by MIPS Technologies (which is now owned by Imagination Technology in the U.K.,  a rival of ARM).  Six months after completing the Atmel acquisition, Microchip said it would expand both its MIPS-based PIC32 MCU product line and Atmel’s ARM-based SAM series.  Microchip has promised to “remain core agnostic, fitting the best solution with the right customer and for the right application.”

Meanwhile, Cypress in Silicon Valley moved into eighth place in the MCU ranking with sales increasing 15% in 2016 to about $622 million.  Cypress boosted its presence in MCUs when it acquired Spansion for about $5.0 billion in stock in March 2015.  Originally spun out of Advanced Micro Devices as a NOR flash memory supplier, Spansion had purchased Fujitsu Semiconductor’s Microcontroller and Analog Business in 2013 for $110 million as part of its efforts to expand beyond nonvolatile storage ICs. Spansion also licensed ARM’s 32-bit CPU cores for microcontrollers in 2013.  Cypress’ increase in microcontroller sales was partly a result of having a full year of revenue from Spansion’s MCU business but also growth in the company’s programmable system-on-chip (PSoC) products, which combine microcontroller functionality with user-configurable peripherals of mixed-signal and digital functions that are targeted at end-use applications.

The biggest decline in the MCU leader list was posted by Samsung, which saw its sales drop 14% in 2016, primarily because of weakness in the smartcard microcontroller market.  Samsung sells MCUs to OEMs but also serves in-house needs for its own brands of consumer electronics, computers, and communications systems (i.e., smartphones).

Immersion-based self-aligned quadruple patterning is combined with EUV lithography block patterning to achieve metal layers with pitches as small as 32nm.

BY JOOST BEKAERT and MING MAO, imec, Leuven, Belgium

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At the 2017 SPIE Advanced Lithography conference, imec – in close collaboration with its suppliers – presented an industry relevant platform for patterning the most advanced back-end-of- line metal layers, conform with the foundry N5 technology node. Imec’s solution includes two scenarios for EUV lithography insertion, as well as proposals for design rules, masks, photoresists, etching, metrology and variation assessment. In this article, one of these scenarios is described in more detail. It combines immersion-based self-aligned quadruple patterning with EUV lithography block patterning, to achieve metal layers with pitches as small as 32nm. To assess the platform’s suitability for high- volume manufacturing, the uniformity of the layers and their local variability is discussed.

The patterning of advanced logic back-end-of-line layers

As we move towards more advanced technology nodes, the patterning of critical back-end-of-line (BEOL) metal layers with ever more aggressive pitches (e.g. 32nm) has become very challenging. In these BEOL layers, typically, trenches are created which are then filled with metal in a final metallization step. In order to create a disconnection in the continuous trenches, block layers perpendicular to the trenches are added, resulting in small metal tip-to-tips. In the industry, various options are considered to pattern the most aggressive BEOL layers and blocks. One option is to use immersion lithography in combination with so-called self-aligned quadruple patterning (SAQP) for the metal lines, and triple patterning for the block layers. This option however requires a triple block mask and a triple litho-etch process flow, which adds to the cost and complexity of the proposed solution. Another option is to pattern the BEOL metal layers directly with EUV lithography (EUVL) in one single exposure. Although this direct EUVL integration flow is very simple and cost-effective, pattern fidelity (e.g. the shape of the pattern) and pattern variability, as well as mask making are expected to be extremely challenging, especially for very small tip-to-tips.

One of the alternatives imec is evaluating is a ‘hybrid’ option, in which immersion-based SAQP of metal lines is combined with a direct EUV print of the block layer – using ASML’s NXE:3300 scanner.

The imec N7 (iN7) EUV platform

To evaluate the viability of this ‘SAQP + EUV block patterning’ option, imec makes use of its iN7 platform. This platform has been developed to evaluate EUV patterning of advanced logic BEOL layers. The platform considers two layers: metal1, with 42nm pitch, and metal2, with 32nm pitch and 7.5 track design. Via1 connects the two metal layers using a dual damascene process flow. With these aggressive pitches, iN7 corresponds to IDM N7 and foundry N5 requirements for the BEOL. The patterning of both metal1 and via1 can be achieved through EUV single exposure. The iN7 platform is used to evaluate the hybrid immersion/EUVL solution for patterning metal2.

Optimizing design rules, mask and etch process

Prior to printing and evaluating the pattern, considerable efforts and innovations were performed in various litho-related areas. First, imec developed compliant design and design rules to support the possible patterning schemes. Also, an appropriate resist material was chosen for the EUV block process, and its impact on the optical proximity correction was studied – leading to a 2D OPC full-chip model. This model and other computational lithography techniques were used to design and fabricate the right EUV block masks. And finally, new chemistries and novel integration schemes for the etch process have been developed.

Creating SAQP lines and EUV blocks

SAQP (or self-aligned quadruple patterning) is a double spacer technique that is already well established in industry.Basically,this process uses one lithography step and additional deposition and etch steps to define spacer- like features.

Imec’s process flow starts from metal2 core lines, i.e. a (pre) pattern of lines created by immersion lithography (using the ASML NXT:1970i immersion scanner). On top of this pattern, a layer of spacer material is deposited. Then, the spacer is etched and the core material is removed. This second ‘core’ pattern is then used to apply the second spacer, by re-iterating the sequence of spacer deposition, spacer etch and core removal. After these steps, each edge of a core line results in a doublet of spacer lines. As a final result, groups of 6 spacer lines are created with a 4x denser pitch (16nm half pitch) than the initial (pre)pattern. This grating is then transferred into SiN, leaving a pattern of SiN lines on top of a TiN layer (FIGURE 1).

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In a next step, block features are added on top of the SAQP pattern. First, spin-on carbon (SoC) is coated on top of the spacer pattern. After resist coating, EUV exposure on the ASML NXE:3300 scanner then creates the block features in the resist material on top of the SoC. After SoC etch, pillar-like SoC block features of 65nm height stand on the spacer lines. This joint SAQP + block pattern is then patterned into the underlying TiN layer, which serves as a hard mask. By etching the trenches within this pattern into the low-k dielectric layer below, and metallizing them, the final metal2 pattern is obtained. The width of the block features determines the metal2 tip-to-tip critical dimension (FIGURE 2).

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Assessing pattern fidelity and local variability

An important part of this work is to qualify the pattern fidelity and variability, as this will contribute to the viability of the proposed solutions for industrial manufac- turing. At this small pitch of 32nm, even minor process variations in EUV lithography may have significant impact on the device performance. Such variations are due to overlay and CD uniformity, but also to stochastic effects in the resist.

In particular, the uniformity of the width and length of the block features are important parameters. The width of a block at the location of a trench determines the resulting metal tip-to-tip on that trench. The final target for the iN7 design is to achieve a critical dimension of 21nm metal tip-to-tip after low-k etch. The experiments show that the critical dimension is sufficiently uniform over the wafer. With further fine-tuning, it is expected to remain below 1nm 3sigma. Also the local variation of the block width and placement are important and determine the overlap of the metal line-end with the via that connects to a layer above or below. The major contributor to the local variation turns out to be the stochastic noise, coming from statistical variations in how the available photons interact with the resist. Added to the overlay (which involves the ability of the scanner to align the various layers accurately on top of each other), an edge placement error of the metal tip position of ~5nm 3sigma is obtained. Whether this provides sufficient overlap with the via layer, will depend on the design rule. For example, if no direct neighboring vias are allowed, there will be sufficient margin through the design extension of the metal tip over the via.

Another critical dimension is related to the length of the block, as this will be critical in determining the metal trench ‘blocking’ efficiency. A too short block feature could lead to an incomplete cut of the metal trench, and a too long feature can pinch neighboring metal trenches. Ideally, the block end is positioned halfway the spacer line. The maximum budget for the variation of the block end vs. the spacer edges is +/- 8nm. The dominant consumers of this budget are again the overlay and stochastics, adding to a local variation of ~6nm 3sigma. Thus, with a 3sigma requirement and if other contrib- utors (such as intra-wafer CDU) can be kept small, the spacer width (16nm) is expected to provide sufficient budget to enable the SAQP + block technology for the iN7 (FIGURE 3).

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Towards EUV implementation for high-volume manufacturing

Imec researchers have investigated the use of SAQP in combination with a single EUV blocking step for printing the critical 32nm pitch metal2 layer in the back-end-of- line. An important finding is that the current imaging performance of the ASML NXE:3300 is sufficient to print the metal2 block layer. The results clearly show the successful integration capability of the EUVL defined block. However, overlay and stochastics came forward as key attention points and will need further improvement, especially if further downscaling is pursued.

The proposed solution is a viable alternative to SAQP + immersion triple block patterning of the 32nm metal layer. From a cost perspective, a 20% cost reduction can be expected from the ‘hybrid’ solution with direct EUV block print, and EUV print of the vias. An additional cost reduction of 3% is expected from a scenario that uses only EUV in one single exposure for patterning the BEOL metal layers. First results point towards pattern fidelity and mask making as the main challenges for this option. Optimizations for this option are ongoing.

As pitch-only scaling doesn’t meet the full require- ments for the foundry N5 node, the solutions have been complemented by co-optimizing the technology and the standard cell libraries, resulting in significantly lower standard cell heights. This will allow a full node definition whereby the wafer cost increase of scaling boosters (approx. 3%) is offset by an area reduction gain of approx. 21%.

Including the proposals for design rules, masks, photo- resists, etching and metrology, for which imec worked in close collaboration with equipment and material suppliers, all these studies form the first comprehensive solution towards EUVL enablement for high-volume manufacturing.

With its unique characteristics, FD-SOI is generating increasingly strong interest from major players in the semiconductor ecosystem for a very wide range of markets.

BY MANUEL SELLIER, Soitec, Bernin (Grenoble), France

Fully depleted silicon-on-insulator or FD-SOI is the only technology bringing together two substantial characteristics of CMOS transistors: 2D planar transistor structure and fully depleted operation. It relies on a unique substrate whose layer thicknesses are controlled at the atomic scale. FD-SOI offers remarkable transistor performance with one of the best power, performance, area and cost tradeoffs (PPAC) among all advanced CMOS technologies. In addition, FD-SOI has numerous other unique advantages including back bias ability, very good transistor matching, near threshold supply capability, ultra-low sensitivity to radiation and very high intrinsic transistor speed, which allows it to handle mmWave frequencies.

All these key features are progressively making FD-SOI a de facto technology for many applications including entry-level application processors for smartphones, system-on- chip (SoC) devices for autonomous driving and IoT, and all mmWave applications such as 5G transceivers and radar systems for automotive electronics.

FD-SOI technology is supported by multiple foundries and IDMs with full technology offerings now available for the 28nm and 22nm nodes and emerging for the 65nm and 12nm nodes. With this global ecosystem in place, FD-SOI is ready for applications development for diversified markets.
There are several striking characteristics of FD-SOI substrates that give this technology unique advantages. This article summarizes the latest advances and the various elements of the global ecosystem that supportwidespread implementation of FD-SOI as well as the applications that most benefit from it.

The heart of FD-SOI

Everything in FD-SOI technology starts with the substrate. The substrate directly defines the transistor architecture, as shown in FIGURE 1. To allow the fully depleted operation of transistors, the thickness of the top silicon layer defining the device channel represents a real challenge, with the thickness target typically around 60 Å or 11 atomic layers. Given the consumption of silicon material during device fabrication, a 120 Å incoming top silicon specification is usually required by foundries. Uniformity is another very challenging specification needed to keep transistor variability as low as possible. Uniformity of +/-5 Å or 1 atomic layer is typically considered essential. Buried oxide (BOx) thickness also must be very thin – around 20nm – to maximize electrostatic control in the transistor channel due to the ground plane effect.

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Manufacturing a 300mm piece of crystalline silicon with a thickness specification as low as 11 +/-1 atomic layers is understandably difficult. Ten years ago, it sounded unachievable so people studied other paths to enable fully depleted transistors [1]. But it is now possible.

Fabrication relies on the well-known Smart Cut TM process (FIGURE 2). As shown, wafer A first undergoes an oxidation step followed by high-dose ion implantation, creating a weakened layer just beneath the surface. After careful cleaning steps, wafer A is bonded to wafer B through molecular-bonding technology. Splitting is then induced at the precise location of the weakened zone of wafer A. Finally, the formed SOI wafer is subjected to other smoothing process steps to achieve the targeted thickness specification. It is noteworthy that high-quality wafer A can be recycled for further reuse, making Smart Cut the most cost- effective solution for SOI manufacturing.

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The FD-SOI substrate-manufacturing process is now fully mature. In particular, thickness uniformity is very well controlled at all levels, from transistor to wafer, as shown in FIGURE 3. This ensures a very low level of transistor variability.

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When less is more

The way of getting more performance out of silicon below 28nm node adds more complexity to the manufacturing process. Consequently, as illustrated in FIGURE 4, the smaller nodes get, the greater number of masks are needed to create chips. This increases manufacturing costs as well as other non-recurring engineering costs including design flow, design verification, mask sets and more.

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On the other hand, FD-SOI is a simple technology from a manufacturing standpoint. In fact, it offers more perfor- mance while decreasing the manufacturing process complexity. Most of the channel engineering work is actually done directly at the substrate level, making FD-SOI easier to implement than bulk silicon, as major foundries have reported [2] [3].

The next level of transistor performance

In addition to simpler manufacturing, FD-SOI offers other substantial benefits, as depicted below and summarized in FIGURE 5.

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1. Better design flexibility through body bias

The thin BOx of FD-SOI not only enhances electro- static control of the channel, but also makes it possible to completely tune the threshold voltage through back biasing. All the complex Vth adjustment techniques through channel doping can be avoided. Low, mid-range and high Vth can be achieved simply through back-gate polarization. The thin BOx behaves like a real second gate and, most importantly, it can be used dynami- cally. This means that the same functional block can operate under high or low power, on demand. Back bias potential is huge: selective body bias for critical path improvements [4], process variation compensation [5] and reliability drift compensation [6]. Full back biasing is a very unique feature, only achievable with SOI on thin BOx technology.

2. Power-performance-area-cost tradeoff: Best PPAC of all planar technologies.

Thanks to simpler manufacturing, better control of random mismatch, minimizing of junction leakage and capacitances, enhanced electrostatic control through fully depleted transistor operation and the possibility of tuning body bias, FD-SOI technology presents the best power- performance-area-cost tradeoff (PPAC) among all planar technologies.

3. Ultra-low power through near-threshold supply voltage

Almost all CMOS technologies achieve their best energy efficiency – i.e., the lowest amount of energy per function, regardless of the frequency – at around 0.4 V supply voltage, often referred to as Vdd [7]. At this level of supply voltage, variability management is a real challenge. Thanks to body bias and to its intrinsic low-variability characteristics, FD-SOI can achieve very low supply voltages. More generally, the ability to lower the supply voltage, although not necessarily as low as 0.4 V, is a real challenge in many applications in which power is a greater challenge than performance. Given the fact that dynamic power scales with Vdd2, a technology like FD-SOI that is capable of strong power savings through tremendous supply voltage reduction presents a unique advantage.

4. Best RF-CMOS technology with almost 2 times maximum frequency over 3D devices

Integrating as many analog/RF functions as possible into a single RF-CMOS silicon platform is becoming an increasingly important issue in many markets for obvious cost and power reasons. However, one limitation of RF-CMOS platforms is the limited ability to increase frequency, especially in the mmWave spectrum (30 GHz and above). This is a bigger issue with 3D devices such as FinFETs, which must carry very strong parasitic capaci- tances due to their 3D structures [8]. As a result, SiGe- Bipolar platforms are often used for this frequency range. FD-SOI is a planar technology and, as such, it should not suffer from the limitations of 3D devices. Ft/Fmax in the range of 325-350 GHz have been reported [3], allowing full usage of the mmWave spectrum up to 100 GHz and giving FD-SOI RF-CMOS platforms a bright future in many applications.

5. Enhanced reliability

Low sensitivity to high-energy particles is another key characteristic of FD-SOI. High-energy particles can interact with silicon and generate a significant amount of charges capable of flipping transistor logic state, thus increasing the soft errors rate (SER). FD-SOI devices are completely isolated from the substrate due to the BOx layer. This means that any charge generated in the substrate is unlikely to modify the device logic state. In short, FD-SOI is much less sensitive to SER [9]. This has very important consequences for safety-critical devices such as autonomous car systems.

6. Outstanding analog transistor characteristics

Often, analog designers have to make their designs work with more and more degraded transistors as technology shrinks. Meeting speed, noise, power, leakage and variability requirements is increas- ingly challenging. By providing a transistor with improved matching, gain and parasitic, FD-SOI can greatly simplify device design [10]. Moreover, the back bias has potential for the design of many new analog structures [11].

FD-SOI’s growing use at foundries

Some of the most pioneering work with FD-SOI has been done at semiconductor foundries around the world.

STMicroelectronics adopted FD-SOI technology in 2012 [12] and started several projects. The company demonstrated an ARM-based application processor for smart-phones with 3 GHz+ operating frequency on 28nm FD-SOI [13]. The technology is now used at STMicroelectronics for many diversified markets [14] [15].

In 2014, Samsung announced the adoption of 28nm FD-SOI technology for its foundry division [15]. Mass production maturity was reached in 2016 [2], and the first product release was announced recently [16] [17].

In 2015, GLOBALFOUNDRIES developed a 22nm FD-SOI technology called 22FDX [18], which it positioned as offering the best performance/cost ratio. This FD-SOI technology platform achieved performance close to 16nm/14nm FinFET at a cost similar to 28nm bulk silicon [19]. The first commercial product was announced in February 2017 by GLOBALFOUNDRIES and Dream Chip Technologies [20]. GLOBALFOUNDRIES’ technology is now almost fully qualified, with volume ramp-up expected this year.

In Asia, the Chinese foundry Huali has announced its intention to include 22nm FD-SOI technology in its fab2 plan [21], offering the Chinese market greater access to FD-SOI technology.

In Japan, Renesas’ experience with FD-SOI includes work on a very low-power FD-SOI technology called silicon- on-thin-BOx (SOTB), which targets low-power MCU markets. This technology has been supported by the LEAP initiative (Low-Power Electronics Association and Project) and is now available in 65nm. Renesas has reported very low-power consumption with this platform, as small as a tenth of that achieved using bulk silicon.

IP/CAD status and roadmap

The design ecosystem is well established for 28nm FD-SOI with complete libraries and foundation IP and growing at a fast pace for 22nm technology. EDA companies are on board and developing IP ported to FD-SOI.

In September 2016, GLOBALFOUNDRIES announced a new partner program called FDXceleratorTM to facil- itate 22FDX SoC design and reduce time to market for its customers including Synopsys, Cadence, INVECAS, VeriSilicon, CEA-Leti, Dream Chip and Encore Semi [22]. In December 2016, the foundry announced the addition of eight new partners to its growing FDXcel- erator program including Advanced Semiconductor Engineering (ASE Group), Amkor Technology, Infosys, Mentor Graphics, Rambus, Sasken, Sonics and Quick- Logic [23].

As for the technology roadmap, FD-SOI is available on a wide range of technology nodes from 65nm to 12nm with visibility down to 7nm. Building on the success of its 22FDX offering, in 2016 GLOBALFOUNDRIES unveiled a new 12nm FD-SOI semiconductor technology called 12FDX [24]. Staying with fully depleted planar processing allows the foundry to take advantage of the low parasitic capacitance, avoid the complex lithog- raphy steps required by equivalent 3D transistors, and leverage back biasing to boost transistor performance, especially at low supply voltages. Customer product tape-outs are expected to begin by the end of 2017.

Leti, which pioneered FD-SOI development 15 years ago, worked with GLOBALFOUNDRIES on the 22FDX and 12FDX platforms. The organization recently developed test devices on 10nm FD-SOI technology and produced models for 10nm and 7nm on FD-SOI. Leti strongly believes that FD-SOI can be scaled down to 7nm.

Both Samsung and GLOBALFOUNDRIES plan to have embedded non-volatile memory integrated into their FD-SOI technology platforms by 2018 [2] [3].

FD-SOI traction in power and analog/RF integration ThankstothegrowingmaturityoftheFD-SOIecosystem, there is now a wide range of applications seeing strong differentiation possibilities through FD-SOI. These include single-chip solutions for entry-level mobile communications, general purpose application processors, image signal processors, SoC for set-top boxes, embedded computer vision, microcontrollers, mixed-signal applications such as transceivers, GPS/satellite receivers, wi-fi/ BT combos and mmWave radar systems.

For all these applications, power budget is typically very limited and must be balanced with performance targets. A good example of this can be found in embedded computing applications such as ADAS, where designers must constantly find compromises to achieve the required performance with a very limited power budget, typically around 3 W. For all embedded computing applications, FD-SOI – and its ability to run on very low supply voltages – is gaining momentum as the reference technology.

In addition, RF/analog integration is often key for these applications. Having best-in-class RF-CMOS technology available on the same silicon die as the digital parts is a unique advantage of FD-SOI. It opens up possibilities for single-chip solutions covering a wide range of functions. This is particularly advantageous in entry-level markets such as low-end mobile, where the price pressure is so great that integration must be pushed to its limits, or in mmWave applications including radar and 5G transceivers, where the mmWave RF functions can be integrated on the same die as the computing functions.

A new wave of ground-breaking products

The list of FD-SOI-based products is increasing at a very fast pace, with multiple product announcements over the past months.

In September 2016, Huami (a Xiaomi partner company) introduced a new fitness smartwatch that includes a FD-SOI-based global positioning system (GPS) chip demonstrating record energy efficiency (FIGURE 6) [25]. The chip allows the watch to reach an unsurpassed battery life of 35 hours with the GPS turned on, which represents two to five times more than today’s similar devices. The chip, revealed in February 2016 at the International Solid- State Circuits Conference (ISSCC) in San Francisco [27], dramatically lowers power usage and opens the door for always-on GPS applications in smartwatches, smart-phones, drones and a large number of devices for the IoT.

Also in 2016, Mobileye posted on its website that its next EyeQ4 product family dedicated to level3 autonomous driving will be based on FD-SOI technology [26] (FIGURE 7).

Screen Shot 2017-04-27 at 12.02.09 PM

In March 2017, NXP released two general-purpose processor families (i.MX7ULP and i.M8X) [16] [17] based on Samsung’s 28FDS FD-SOI technology for ultra-low power consumption and rich graphics in battery-powered applications (see NXP roadmap FIGURE 8). NXP reported a deep-sleep suspended power consumption of 15 μW or less for its i.MX7ULP product, 17 times less in comparison to previous low-power bulk devices, while the dynamic power efficiency improved by 50 percent. This high-performance, low-power solution is optimized for customers developing IoT, home control, wearable and other applications that spend a significant amount of time in standby mode with short bursts of performance-intense activity that require exceptional graphics processing.

Screen Shot 2017-04-27 at 12.02.17 PM

In March 2017, Eutelsat Communications and STMicroelectronics announced a new-generation SoC for interactive applications that represents a step down in the overall cost of interactive satellite terminals while reducing power consumption [14].

On the 22nm side, Dream Chip announced the industry’s first 22nm FD-SOI product for a new ADAS SoC for automotive computer-vision applications [20]. The SoC device (FIGURE 9) offers high- performance image acquisition and processing capabilities and supports convolutional neural network (CNN) vision workloads to meet the demand for complex automotive object detection and processing.

Screen Shot 2017-04-27 at 12.02.27 PM

The 22nm FD-SOI product portfolio is expected to grow significantly in the coming year as the technology ramps up.

Adding fabs to meet overall FD-SOI demand

Faced with the growing interest of FD-SOI, particularly in China, foundries are organizing themselves to build up enough production capacity. In February 2017, GLOBALFOUNDRIES announced plans to expand the capacity of its Fab 1 facility in Dresden by 40 percent by 2020. Dresden will continue to be the center for FDX technology development [27].

In China, GLOBALFOUNDRIES and the Chengdu munici- pality have announced a partnership to build a fab. The partners plan to establish a 300mm fab to support the growth of the Chinese semiconductor market and to meet accelerating global customer demand for 22FDX [27]. The fab will begin producing mainstream process technologies in 2018 and then focus on manufacturing GLOBALFOUNDRIES’ commercially available 22FDX process technology, with volume production expected to start in 2019.

With these two announcements, GLOBALFOUNDRIES will have a future production capacity of more than 2 million FD-SOI wafers per year.

Regarding FD-SOI substrate manufacturing capacity, Soitec owns one 300mm fab in France and has another one in Singapore (currently in standby mode) with a combined global capacity of 1.5 million wafers per year (for manufacturing FD-SOI and other emerging SOI products). The company has plans to go beyond that to meet additional customer demand.

Conclusion

Growing interest in FD-SOI reflects today’s new paradigm for semiconductor technologies. Customers are demanding for more computing capability with drastically reduced power consumption, enabled by enhanced analog/RF integration. With its unique characteristics, FD-SOI is generating increasingly strong interest from major players in the semiconductor ecosystem for a very wide range of markets, especially for embedded computing applications. FD-SOI is now a mainstream technology, which device designers are leveraging for key competitive advantages.

Acknowledgements

The author would like to warmly thank the Soitec team (Christophe Maleville, Bich-Yen Nguyen, Thomas Piliszczuk, Alexandra Givert, and Camille Dufour) for their valuable contribution and constructive discussions.

References

1. H. Chenming, “FinFET-a self-aligned double-gate MOSFET scalable to 20 nm,” IEEE Transactions on Electron Devices, p. 2320–2325, December 2000.
2. Y. Jeon (SAMSUNG), “The industry’s first mass-produced FD-SOI technology for the IoT era, with single design platform benefits,” in SOI Industry Consortium workshop, Tokyo, 2016.
3. J. Schaeffer (GLOBALFOUNDRIES), “FDX Rising,” in GLOBAL- FOUNDRIES Technology Conference, San Jose, 2016.
4. W. Abbey (ARM), “Realize the potential of FD-SOI,” in SOI Industry Consortium workshop, San Jose, 2016.
5. P. Flatresse (ST), “FD-SOI ULV, Body Biasing & Demonstrators,” in LETI days FDSOICE Workshop, GRENOBLE, 2015.
6. C. Ndiaye (ST), “Performance vs. reliability adaptive body bias scheme in 28nm & 14nm UTBB FD-SOI nodes,” Microelectronics Reliability, 2016.
7. B. Zha, in VLSI symposium, 2006.
8. IMEC, chez VLSI Design, 2010.
9. P. Roche (ST), “Technology downscaling worsening radiation effects in bulk: SOI to the rescue,” in IEDM, 2013.
10. G. Cesana (ST), “Advances in Applications and Ecosystem for the FD-SOI Technology,” in LETI days FDSOICE Workshop, GRENOBLE, 2015.
11. A. Cathelin (ST), “On the usage of FBB for inverter-based Analog and RF 28nm UTBB FD-SOI circuits : example of a 450MHz Gm-C filter with IIP3> 1dBv over a 0.7-1V power supply,” in LETI Days FDSOICE Workshop, GRENOBLE, 2015.
12. STMicroelectronics Announces Its 28nm FD-SOI Technology Is Ready for Manufacturing in Its Leading-Edge Crolles Fab, ST Press Release, 2012.
13. ST-Ericsson brings PC speeds to mobile devices: First 3Ghz smartphone prototype demo at Mobile World Congress, STE Press Release, February 20, 2013.
14. EUTELSAT and STMicroelectronics announce low-cost, low-power, system-on-chip for interactive satellite terminals, EUTELSAT Press Release, March 8, 2017.
15. G. Desoli (ST), «A 2.9TOPS/W deep convolutional neural network SoC in FD-SOI 28nm for intelligent embedded systems,» chez International Solid-State Circuits Conference (ISSCC), 2017.
16. Samsung and STMicroelectronics Sign Strategic Agreement to Expand 28nm FD-SOI Technology, Samsung/STMicroelectronics Press Release, May 14, 2014.
17. NXP Taps into FD-SOI Technology to Enable the Industry’s Lowest Power General Purpose Applications Processors, NXP Press Release, March 13, 2017.
18. NXP Delivers Increased Safety, Reliability and Scalability to Industrial Applications with New i.MX 8X Processors, NXP Press Release, March 14, 2017.
19. GLOBALFOUNDRIES Launches Industry’s First 22nm FD-SOI Technology Platform, GLOBALFOUNDRIES Press Release, July 13, 2015.
20. S. Jha (GLOBALFOUNDRIES), “The Right Technology at the Right Time,” in SOI Industry Consortium workshop, Shanghai, 2015.
21. Dream Chip Technologies Presents First 22nm FD-SOI Silicon of New Automotive Driver Assistance SoC, DREAM CHIP Press Release, February 27, 2017.
22. R. Merritt, “China Defends Big Chip Bet – Inside Huali’s $5.9 billion bet on Fab 2,” EETIMES, January 12, 2017.
23. GLOBALFOUNDRIES Unveils Ecosystem Partner Program to Accelerate Innovation for Tomorrow’s Connected Systems, GLOBALFOUNDRIES Press Release, September 8, 2016.
24. GLOBALFOUNDRIES Expands Partner Program to Speed Time- to-Market of FDXTM Solutions, GLOBALFOUNDRIES Press Release, December 15, 2016. www.solid-state.com
25. GLOBALFOUNDRIES Extends FDXTM Roadmap with 12nm FD-SOI Technology, GLOBALFOUNDRIES Press Release, September 8, 2016.
26. J. Yoshida, «Sony-Inside Huami Watch: Is It Time for FD-SOI?,» October 4, 2016.
27. K. Yamamoto (SONY), “A 0.7V 1.5-to-2.3mW GNSS receiver with 2.5-to-3.8dB NF in 28nm FD-SOI,” in International Solid-State Circuits Conference (ISSCC), 2016.
28. Mobileye, “The Evolution of EyeQ,” [Online]. Available: http:// www.mobileye.com/our-technology/evolution-eyeq-chip/. [Accessed 29 March 2017].
29. GLOBALFOUNDRIES Expands to Meet Worldwide Customer Demand, GLOBALFOUNDRIES Press Release, February 9, 2017.

By Douglas G. Sutherland and David W. Price

Author’s Note: The Process Watch series explores key concepts about process control—defect inspection and metrology—for the semiconductor industry. Following the previous installments, which examined the 10 fundamental truths of process control, this new series of articles highlights additional trends in process control, including successful implementation strategies and the benefits for IC manufacturing.

While working at the Guinness® brewing company in Dublin, Ireland in the early-1900s, William Sealy Gosset developed a statistical algorithm called the T-test1. Gosset used this algorithm to determine the best-yielding varieties of barley to minimize costs for his employer, but to help protect Guinness’ intellectual property he published his work under the pen name “Student.” The version of the T-test that we use today is a refinement made by Sir Ronald Fisher, a colleague of Gosset’s at Oxford University, but it is still commonly referred to as Student’s T-test. This paper does not address the mathematical nature of the T-test itself but rather looks at the amount of data required to consistently achieve the ninety-five percent confidence level in the T-test result.

A T-test is a statistical algorithm used to determine if two samples are part of the same parent population. It does not resolve the question unequivocally but rather calculates the probability that the two samples are part of the same parent population. As an example, if we developed a new methodology for cleaning an etch chamber, we would want to show that it resulted in fewer fall-on particles. Using a wafer inspection system, we could measure the particle count on wafers in the chamber following the old cleaning process and then measure the particle count again following the new cleaning process. We could then use a T-test to tell if the difference was statistically significant or just the result of random fluctuations. The T-test answers the question: what is the probability that two samples are part of the same population?

However, as shown in Figure 1, there are two ways that a T-Test can give a false result: a false positive or a false negative. To confirm that the experimental data is actually different from the baseline, the T-test usually has to score less than 5% (i.e. less than 5% probability of a false positive). However, if the T-test scores greater than 5% (a negative result), it doesn’t tell you anything about the probability of that result being false. The probability of false negatives is governed by the number of measurements. So there are always two criteria: (1) Did my experiment pass or fail the T-test? (2) Did I take enough measurements to be confident in the result? It is that last question that we try to address in this paper.

Figure 1. A “Truth Table” highlights the two ways that a T-Test can give the wrong result.

Figure 1. A “Truth Table” highlights the two ways that a T-Test can give the wrong result.

Changes to the semiconductor manufacturing process are expensive propositions. Implementing a change that doesn’t do anything (false positive) is not only a waste of time but potentially harmful. Not implementing a change that could have been beneficial (false negative) could cost tens of millions of dollars in lost opportunity. It is important to have the appropriate degree of confidence in your results and to do so requires that you use a sample size that is appropriate for the size of the change you are trying to affect. In the example of the etch cleaning procedure, this means that inspection data from a sufficient number of wafers needs to be collected in order to determine whether or not the new clean procedure truly reduces particle count.

In general, the bigger the difference between two things, the easier it is to tell them apart. It is easier to tell red from blue than it is to distinguish between two different shades of red or between two different shades of blue. Similarly, the less variability there is in a sample, the easier it is to see a change2. In statistics the variability (sometimes referred to as noise) is usually measured in units of standard deviation (σ). It is often convenient to also express the difference in the means of two samples in units of σ (e.g., the mean of the experimental results was 1σ below the mean of the baseline). The advantage of this is that it normalizes the results to a common unit of measure (σ). Simply stating that two means are separated by some absolute value is not very informative (e.g., the average of A is greater than the average of B by 42). However, if we can express that absolute number in units of standard deviations, then it immediately puts the problem in context and instantly provides an understanding of how far apart these two values are in relative terms (e.g., the average of A is greater than the average of B by 1 standard deviation).

Figure 2 shows two examples of data sets, before and after a change. These can be thought of in terms of the etch chamber cleaning experiment we discussed earlier. The baseline data is the particle count per wafer before the new clean process and the results data is the particle count per wafer after the new clean procedure. Figure 2A shows the results of a small change in the mean of a data set with high standard deviation and figure 2B shows the results of the same sized change in the mean but with less noisy data (lower standard deviation). You will require more data (e.g., more wafers inspected) to confirm the change in figure 2A than in figure 2B simply because the signal-to-noise ratio is lower in 2A even though the absolute change is the same in both cases.

Figure 2. Both charts show the same absolute change, before and after, but 2B (right) has much lower standard deviation. When the change is small relative to the standard deviation as in 2A (left) it will require more data to confirm it.

Figure 2. Both charts show the same absolute change, before and after, but 2B (right) has much lower standard deviation. When the change is small relative to the standard deviation as in 2A (left) it will require more data to confirm it.

The question is: how much data do we need to confidently tell the difference? Visually, we can see this when we plot the data in terms of the Standard Error (SE). The SE can be thought of as the error in calculating the average (e.g., the average was X +/- SE). The SE is proportional to σ/√n where n is the sample size. Figure 3 shows the SE for two different samples as a function of the number of measurements, n.

Figure 3. The Standard Error (SE) in the average of two samples with different means. In this case the standard deviation is the same in both data sets but that need not be the case. With greater than x measurements the error bars no longer overlap and one can state with 95% confidence that the two populations are distinct.

Figure 3. The Standard Error (SE) in the average of two samples with different means. In this case the standard deviation is the same in both data sets but that need not be the case. With greater than x measurements the error bars no longer overlap and one can state with 95% confidence that the two populations are distinct.

For a given difference in the means and a given standard deviation we can calculate the number of measurements, x, required to eliminate the overlap in the Standard Errors of these two measurements (at a given confidence level).

The actual equation to determine the correct sample size in the T-test is given by,

Equation 1

Equation 1

where n is the required sample size, “Delta” is the difference between the two means measured in units of standard deviation (σ) and Zx is the area under the T distribution at probability x. For α=0.05 (5% chance of a false positive) and β=0.95 (5% chance of a false negative), Z1-α/2 and Zβ are equal to 1.960 and 1.645 respectively (Z values for other values of α and β are available in most statistics textbooks, Microsoft® Excel® or on the web). As seen in Figure 3 and shown mathematically in Eq 1, as the difference between the two populations (Delta) becomes smaller, the number of measurements required to tell them apart will become exponentially larger. Figure 4 shows the required sample size as a function of the Delta between the means expressed in units of σ. As expected, for large changes, greater than 3σ, one can confirm the T-test 95% of the time with very little data. As Delta gets smaller, more measurements are required to consistently confirm the change. A change of only one standard deviation requires 26 measurements before and after, but a change of 0.5σ requires over 100 measurements.

Figure 4. Sample size required to confirm a given change in the mean of two populations with 5% false positives and 5% false negatives

Figure 4. Sample size required to confirm a given change in the mean of two populations with 5% false positives and 5% false negatives

The relationship between the size of the change and the minimum number of measurements required to detect it has ramifications for the type of metrology or inspection tool that can be employed to confirm a given change. Figure 5 uses the results from figure 4 to show the time it would take to confirm a given change with different tool types. In this example the sample size is measured in number of wafers. For fast tools (high throughput, such as laser scanning wafer inspection systems) it is feasible to confirm relatively small improvements (<0.5σ) in the process because they can make the 200 required measurements (100 before and 100 after) in a relatively short time. Slower tools such as e-beam inspection systems are limited to detecting only gross changes in the process, where the improvement is greater than 2σ. Even here the measurement time alone means that it can be weeks before one can confirm a positive result. For the etch chamber cleaning example, it would be necessary to quickly determine the results of the change in clean procedure so that the etch tool could be put back into production. Thus, the best inspection system to determine the change in particle counts would be a high throughput system that can detect the particles of interest with low wafer-to-wafer variability.

Figure 5. The measurement time required to determine a given change for process control tools with four different throughputs (e-Beam, Broadband Plasma, Laser Scattering and Metrology)

Figure 5. The measurement time required to determine a given change for process control tools with four different throughputs (e-Beam, Broadband Plasma, Laser Scattering and Metrology)

Experiments are expensive to run. They can be a waste of time and resources if they result in a false positive and can result in millions of dollars of unrealized opportunity if they result in a false negative. To have the appropriate degree of confidence in your results you must use the correct sample size (and thus the appropriate tools) that correspond to the size of the change you are trying to affect.

References:

  1. https://en.wikipedia.org/wiki/William_Sealy_Gosset
  2. Process Watch: Know Your Enemy, Solid State Technology, March 2015

About the Authors:

Dr. David W. Price is a Senior Director at KLA-Tencor Corp. Dr. Douglas Sutherland is a Principal Scientist at KLA-Tencor Corp. Over the last 10 years, Dr. Price and Dr. Sutherland have worked directly with more than 50 semiconductor IC manufacturers to help them optimize their overall inspection strategy to achieve the lowest total cost. This series of articles attempts to summarize some of the universal lessons they have observed through these engagements.

Worldwide semiconductor wafer-level manufacturing equipment (WFE) revenue totaled $37.4 billion in 2016, an 11.3 percent increase from 2015, according to final results by Gartner, Inc. The top 10 vendors accounted for 79 percent of the market, up 2 percent from 2015.

“Spending on 3D NAND and leading-edge logic process drove growth in the market in 2016,” said Takashi Ogawa, research vice president at Gartner. “This spending was driven by momentum for high-end services in data centers and requirements for faster processors and high-volume memory for mobile devices.”

Applied Materials continued to lead the WFE market with 20.5 percent growth in 2016 (see Table 1). The active investment in 3D device manufacturing provided significant momentum in Applied’s etch revenue, specifically in the conductor etch segment. Screen Semiconductor Solutions experienced the highest growth in the market, with 41.5 percent. This was due to a combination of the appreciation of the Japanese Yen against the U.S. dollar, which elevated dollar-based sales estimates and the demand in premium smartphone and data center servers for big data analysis that drove investment in 3D-NAND capacity and leading-edge technology in foundries.

Table 1

Top 10 Companies’ Revenue From Shipments of Total Wafer-Level Manufacturing Equipment, Worldwide (Millions of U.S. Dollars)

Rank 2015

Rank 2014

Vendor

2016 Revenue

2016 Market Share (%)

2015

Revenue

2015 Market Share (%)

2015-2016 Growth (%)

1

1

Applied Materials

7,736.9

20.7

6,420.2

19.1

20.5

2

4

Lam Research

5,213.0

13.9

4,808.3

14.3

8.4

3

2

ASML

5,090.6

13.6

4,730.9

14.1

7.6

4

3

Tokyo Electron

4,861.0

13.0

4,325.0

12.9

12.4

5

5

KLA-Tencor

2,406.0

6.4

2,043.2

6.1

17.8

6

6

Screen Semiconductor Solutions

1,374.9

3.7

971.5

2.9

41.5

7

7

Hitachi High-Technologies

980.2

2.6

788.3

2.3

24.3

8

8

Nikon

731.5

2.0

724.2

2.2

1.0

9

9

Hitachi Kokusai

528.4

1.4

633.8

1.9

-16.6

10

13

ASM International

496.9

1.3

582.5

1.7

-14.7

Others

7,988.0

21.4

7,586.2

22.6

5.3

Total Market

37,407.3

100.0

33,613.7

100

11.3

Source: Gartner (April 2017)

Additional information is provided in the Gartner report “MarketShare: SemiconductorWaferFab Equipment, Worldwide, 2016.” The report provides rankings and market share for the top 10 vendors. In 2015, Gartner changed the segment reporting to focus on wafer-level manufacturing and is no longer providing segment details for die-level packaging or automatic test. This report is limited to wafer-level manufacturing equipment.

Worldwide semiconductor revenue is forecast to total $386 billion in 2017, an increase of 12.3 percent from 2016, according to Gartner, Inc. Favorable market conditions that gained momentum in the second half of 2016, particularly for commodity memory, have accelerated and raised the outlook for the market in 2017 and 2018. However, the memory market is fickle, and additional capacity in both DRAM and NAND flash is expected to result in a correction in 2019.

“While price increases for both DRAM and NAND flash memory are raising the outlook for the overall semiconductor market, it will also put pressure on margins for system vendors of smartphones, PCs and servers,” said Jon Erensen, research director at Gartner. “Component shortages, a rising bill of materials, and the prospect of having to counter by raising average selling prices (ASPs) will create a volatile market in 2017 and 2018.”

PC DRAM pricing has doubled since the middle of 2016. A 4GB module that cost $12.50 has jumped to just under $25 today. NAND flash ASPs increased sequentially in the second half of 2016 and the first quarter of 2017. Pricing for both DRAM and NAND is expected to peak in the second quarter of 2017, but relief is not expected until later in the year as content increases in key applications, such as smartphones, have vendors scrambling for supply.

“With memory vendors expanding their margins though 2017, the temptation will be to add new capacity,” said Mr. Erensen. “We also expect to see China make a concerted effort to join the memory industry, setting the market up for a downturn in 2019.”

Unit production estimates for premium smartphones, graphics cards, video game consoles and automotive applications have improved and contributed to the stronger outlook in 2017. In addition, electronic equipment with heavy exposure to DRAM and NAND flash saw semiconductor revenue estimates increase. This includes PCs, ultramobiles, servers and solid-state drives.

“The outlook for emerging opportunities for semiconductors in the Internet of Things (IoT) and wearable electronics remains choppy with these markets still in the early stages of development and too small to have a significant impact on overall semiconductor revenue growth in 2017,” said Mr. Erensen.