The healthcare opportunity

As companies in the healthcare space capture more and more of the benefits of big data, analytics and AI, these companies— including medical device makers, pharmaceutical firms and related technology companies—will have opportunities for unprecedented growth. For the US medical device market, much of that growth comes out of the Food and Drug Administration’s 2016 Digital Health Innovation Plan, which encourages traditional healthcare companies to adopt new technologies and partner with digital health startups.
Tech giants such as Apple and Google have already demonstrated considerable interest in the growing healthcare market. Apple aims to consolidate a fragmented medical application market by providing all-encompassing platforms such as ResearchKit and CareKit, whereas Google is working to transform the industry through its DeepMind AI solutions. Other
tech companies are also getting involved; for example, Nvidia is using GPU-powered deep learning to diagnose cancers sooner with ultrasound images

The Automotive opportunity

We expect the automotive market to grow the fastest of all the markets, with a CAGR of 11.9%. This is due largely to strong penetration rates of electric and hybrid cars, which require
about twice the semiconductor content of conventional cars, and the strong market potential for autonomous driving. Advanced driver-assistance systems (ADAS), light detection and ranging (LiDAR), infotainment, and safety and convenience functions are gaining more attention as cars become more automated and thereby require more semiconductors per vehicle. According to IC Insights, semiconductor content per vehicle is five times higher for full automation than for partially automated systems. Conventional
cars, however, are still an important catalyst of semiconductor sales. In 2018, conventional car sales accounted for almost 95% of total revenues from the automotive market.

Again, this is the segment with the largest market potential. We expect that it will bring in revenues of US$4.0bn to US$4.7bn in 2022 in ADAS and self-driving assistance use cases
. These will include both inference-based systems, for self-driving and safety assistance in the car and at the edge, and training-based systems, for traffic avoidance mapping. The relative sizes of the two will determine the types of semiconductors that will witness the most growth in demand—GPUs and ASICs for edge
computing and CPUs and FPGAs for cloud computing.

The Industrial opportunity

After automotive, the industrial market will grow the fastest among all application types; we expect a CAGR of 10.8% through 2022. The largest share of that growth will come
from demand for security, automation, solid-state lighting and transportation. We expect demand for semiconductors for security applications to grow the fastest, at a CAGR of 17.8%. This is led by the ongoing push for safer and smarter cities, especially in the AsiaPacific region. Increasing numbers of terrorist attacks on airports and railway stations are spurring investment in advanced perimeter security and access control systems, while a growing emphasis on comfort and convenience is fueling the popularity of fingerprint
door entry systems and PIN and RFID access systems.

The Communications opportunity

Almost 80% of the demand for semiconductors
from the communications market is driven by phones. Though
the phone market is highly saturated, the introduction of 5G,
the continuing high replacement rates of smartphones and the
increasing demand for phones in emerging markets will maintain
a CAGR of 2.2% for the market. And while demand for premium
phones is expected to decline, this will be more than offset by
strong growth in basic phones.

The Consumer electronics opportunity

Semiconductor revenue from consumer electronics applications will be generated by TV devices, driven by the increasing popularity of smart TVs, 4K ultra-HD TVs, 3D programming, video-on-demand content, a preference for large displays, and curved OLEDs. Gaming technology and set-top boxes will also be strong revenue boosters. As a result, the market will grow at a CAGR of 2.2%. Although the wearables market is still relatively small, it will grow the fastest of all the consumer electronics applications, at a CAGR of 6.0%. Revenues for chips for digital players, however, are declining, at a compound annual rate of 2.3%, as more appealing substitutes, such as Netflix and Amazon Prime, grow in popularity. The market for gaming consoles was also saturated through 2018, as consumers turned increasingly to mobile games

The Data processing opportunity

Semiconductor sales in the data processing market, which includes devices such as PCs, ultra-mobiles, tablets, servers and storage devices, will grow at a moderate CAGR of 2.1% through 2022. A considerable portion of the market’s growth will come from storage devices, with a CAGR of 12.3%, as smart functions in end-devices require more semiconductor content. Much of this growth will come from emerging solid-state drive technology, which  overcomes the disadvantages of conventional data drives such as high turnaround time, a tendency to overheat and high power consumption. Strong sales of smartphones and other connected devices will accelerate demand for memory cards and storage devices. There is also pressure in the market to optimize server performance, which will increase the semiconductor content of each device.

The AI and the semiconductor opportunity

AI is the capability of computers to simulate intelligent human behavior and make decisions or recommendations based on sophisticated analysis of data sets and predefined sets of
rules. Semiconductors are instrumental to the development and acceleration of the AI opportunity and thus a key factor in boosting innovation in the field and AI’s potential for growth.
The use of AI typically depends on three kinds of algorithms:
• Machine learning (ML): the practice of using algorithms to parse data, learn from it and then make determinations or predictions about specific situations.
• Deep learning (DL): a type of ML based on analyzing and learning from specific data sets, as opposed to task-specific algorithms.
• Natural language processing (NLP): an approach to analyzing interactions between machines and humans, focusing on how to program computers to process and analyze large amounts of natural language data.