Wind power. Rail. Solar energy. And, perhaps most significantly, electric and hybrid vehicles. Together, these four forces are among the major demand drivers for power devices.
While silicon (Si) still plays a role in power devices, wide-bandgap compound semiconductors like silicon carbide (SiC) and gallium nitride (GaN) are particularly well-suited for power devices thanks to their higher electron mobility, higher critical electric field and higher thermal conductivity. However, as new structures and larger wafer sizes become the norm for power devices, they bring with them distinct manufacturing challenges.
Today, the industry is transitioning from 150mm to 200mm wafers for SiC- and GaN-based devices and 200mm to 300mm wafers for Si-based devices. The reason: larger wafer sizes may help reduce the cost of fabrication. As the wafer size transition occurs, it is important to have a metrology tool that can measure a larger number of data points across the wafer without impacting the overall fab throughput. A loss of throughput adds to cost-of-ownership and may erase savings earned from transitioning to larger wafers.
Efforts at curbing carbon dioxide emissions are stepping up, with more electric vehicles on our roads and the installation of renewable energy sources on the rise. Alongside these advances, the makers of these green technologies are increasing the electrical efficiency of their offerings, with silicon-based power devices being ditched in favour of superior alternatives based on the likes of SiC.
Supporting this move are the superior physical properties of these compounds. Compared with silicon, semiconductors such as SiC have wider-bandgaps, a higher electron saturation velocity, a higher critical electric field and a larger thermal conductivity. Drawing on all these strengths, power transistors offer higher operating frequencies, higher power ratings, elevated operating temperatures, better cooling capability and lower energy loss – just the traits that the market wants.
In recent years, power semiconductor applications have expanded from industrial and consumer electronics to renewable energy and electric vehicles. Looking to the future, the most promising power semiconductor devices will be insulated gate bipolar transistor (IGBT) and power metal oxide semiconductor field effect transistor (power MOSFET) modules.
During the manufacturing of these devices, metal films are deposited on the die of MOSFET and IGBT power devices. These layers of film have two main functions: they connect the elementary cells constituting the power dies to the source (power MOSFET) or emitter (IGBT) and allow for the welding of bond wires on the chip or for the solder bonding, facilitating thermal conduction. Because power devices run high currents at high-operating temperatures, the metal layers need to be properly controlled for electrical properties and thickness to enhance thermal conductivity.
Furthermore, power devices are transitioning from 6-inch to 8-inch wafers; this is happening at the same time as process windows are shrinking. As a result, measuring multi-layer metal thickness accurately and characterizing the uniformity of metal film deposition at the wafer edge has become increasingly important. For example, the front side of wafers requires deposition of a thick metal layer, typically 5µm or more of aluminum alloy. The uniform coverage of aluminum to conduct high currents across the entire wafer is key to device yield and reliability.
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].