Not so long ago, Blu-ray was hailed as a technological advancement in the world of digital video. But in the streaming era, Blu-ray’s luster has faded. However, the technology responsible for the blue laser diode that gave the Blu-ray player its name – gallium nitride (GaN) – is emerging as one of a number of exciting new developments in the semiconductor industry.
Today, GaN is used by the military for radar systems, consumer and automotive electronics as a super-fast power charger and the telecommunications industry in base stations and data servers. GaN offers several advantages over silicon. For starters, GaN offers a significant increase in electron mobility over silicon – 1,000 times more electron mobility, according to various articles – a benefit that leads to other advantages. In addition, GaN is resistant to heat, consumes less energy than other semiconductors, operates at a lower voltage, enables increased miniaturization, offers wider bandwidth and allows for increased electron mobility.
A mother steps on the brakes, bringing her car to a stop as she drops her kids off for dance lessons. At the time, she doesn’t notice anything wrong, but when she takes her car in for its regular service appointment, the mechanic conducts a diagnostic check and discovers that the primary brake system on the car had failed because of a faulty braking controller without anyone realizing it. Fortunately, the car was able to stop successfully due to the vehicle’s system redundancies, and the dealer’s diagnostic test confirms that since that first chip failure, another one has not occurred. The braking systems are behaving normally.
Following that, the dealership sends the information about the braking failure to the manufacturer, where an analyst notes that over the last 60 days, and around the country, six other brake failures traced back to the same controller system have been reported for the same make and model. In each of these situations, the backup system successfully brought each car to a complete stop. And, as in the case with the mother who dropped her kids off at dance class, the analyst looks at the reporting samples for these six other failures and determines that each is isolated and non-recurring.
Over the past ten years, primarily driven by a tremendous expansion in the availability of data and computing power, artificial intelligence (AI) and machine-learning (ML) technologies have found their way into many different areas and have changed our way of life and our ability to solve problems. Today, artificial intelligence and machine learning are being used to refine online search results, facilitate online shopping, customize advertising, tailor online news feeds and guide self-driving cars. The future that so many have dreamed of is just over the horizon, if not happening right now.
The term artificial intelligence was first introduced in the 1950s and used famously by Alan Turing. The noted mathematician and the creator of the so-called Turing Test believed that one day machines would be able to imitate human beings by doing intelligent things, whether those intelligent things meant playing chess or having a conversation. Machine learning is a subset of AI. Machine learning allows for the automation of learning based on an evaluation of past results against specified criteria. Deep learning (DL) is a subset of machine learning (FIGURE 1). With deep learning, a multi-layered learning hierarchy in which the output of each layer serves as the input for the next layer is employed.
Currently, the semiconductor manufacturing industry uses artificial intelligence and machine learning to take data and autonomously learn from that data. With the additional data, AI and ML can be used to quickly discover patterns and determine correlations in various applications, most notably those applications involving metrology and inspection, whether in the front-end of the manufacturing process or in the back-end. These applications may include AI-based spatial pattern recognition (SPR) systems for inline wafer monitoring [2], automatic defect classification (ADC) systems with machine-learning models and machine learning-based optical critical dimension (OCD) metrology systems [1][7].
A recent study shows the radio frequency (RF) filter market growing steadily by nearly $16 billion from 2019 to 2024 at a compound annual growth rate (CAGR) of approximately 20%, according to Technavio. The strong growth in the RF filter market is driven by the increased adoption of 5G technology, the surge in smartphones using 5G, and commercial and consumer devices dependent on internet of things (IoT) applications. Together, these factors are some of the most significant players driving society’s digital transformation.
However, the RF filter market is faced with many of the same challenges the semiconductor industry as a whole is experiencing, including the need to pack more into increasingly smaller spaces. In each successive generation of RF filters, the number of filters has not only steadily increased, the rising number of filters has led to a need for more stringent process monitoring and control. A frequency accuracy, 3σ of 0.1%, requires film thickness control within the same accuracy or better.
Let’s look at one RF filter component, a bulk acoustic wave (BAW) resonator. A BAW is a piezoelectric structure sandwiched between the top and bottom electrodes. The resonant frequency depends on the acoustic velocity and the thickness of the piezoelectric film, and the thickness of the electrode. The thickness of the top electrode as a mass loading layer can be dialed in to generate a frequency shift, which is often used to form a filter passband.
Since the RF filter process is directly correlated to thickness, extremely uniform films (~0.1% or better) need to be deposited. With the additional requirements of 5G to support higher frequencies and increased bandwidth, RF filter device manufacturers employ several different process knobs to tune the devices. For example, we see an increasing trend toward thinner layers to support higher frequencies, the adoption of Sc-doped piezoelectric materials to improve piezoelectric coupling and the addition of temperature compensation SiO2 layers to the stack to improve the temperature coefficient of the resonator.
The ability to trace the genealogy of all the components in an electronic device has been getting more complex for decades. For many industries — automotive, defense, medical and others — the need to locate the source of a problem in near real-time is paramount to gauging the extent of that problem. The extreme case is when the issue occurs with a product that already has been distributed and used in the field. Complicating matters is the fact that the current chip shortage is pushing chip designers to second- and third-tier suppliers for their inventory.
Tracking information is not easily done given the number of times material can change hands during the manufacturing life cycle. Designs can incorporate IP modules from Parties No. 1, No. 2, and No. 3 (figure 1). These designs are blended into a singular chip by the device’s Design House. This chip is then built at Front-end Foundries No. 1 or No. 2. The completed chip can be tested and partially assembled at OSAT A, B, or C. Finished assembly into a multi-chip module (MCM) or printed circuit board (PCB) can take place at Assembly House No. 1 or No. 2 (or happen at Customer A if they provide the IP for a design for a device that can be assembled by Finished Goods Maker No. 1) before it is finally sold by the Design House to the End User or Final Goods Manufacturer A, B, C, D and more for insertion in their end product, after which it is again tested before being sold to the end customer.
This is a very simplified example of how complex a supply chain can be, but it is illustrative nonetheless.
Virtual v. physical traceability
At some point in the supply chain, units receive a physical marker that enables traceability as it progresses through the remaining chain of manufacturing agents. Prior to the application of a marker, reliance on a part’s origin is a function of accounting and accurate recordkeeping. Although this seems simple enough, it is complicated by the transition of “ownership” of the chip as it moves through the supply chain.
Tracing a chip’s origin includes its transformation through multiple physical form factors. These material changes frequently include moving from a lot/wafer/die physical structure to a singulated die on a piece of tape or reel to an assembled die in a package, or in a tray, or as an inserted chip in a multi-chip module or PCB — ultimately ending with the PCB being inserted into a larger form factor, such as an automobile or a computer server. Each time the physical form factor is updated, there is a chance to break traceability in the supply chain if incoming and outgoing product labels are not meticulously documented. This is exacerbated by a lack of standardized data formats and communication frameworks throughout the supply chain. All too often, there is a gap in a unit’s back mapping. Once this occurs, any chance to trace a problem to a source is jeopardized.
It may surprise you, but when it comes to chips in electronic braking systems, airbag control units, and more, automotive manufacturers are still using 10-year-old technology — and with good reason.
For the automotive industry, the reliability, stability, and robustness of electronic components are critical, especially when it comes to meeting the stringent Automotive Electronics Council (AEC) Q100 standards that fabs need to follow. Some in the industry would not only rather keep using proven older chips over new ones, but they might even call for the construction of new fabs for older chips. In other words, tried and true is better than new and improved.