SEMICON West Goes Beyond Smart

When I attend trade shows, I typically expect to see incremental innovations that have a small impact on our industry, but rarely any significant game-changers. This year’s SEMICON West was notably different.

Fuel Cell Technology – The Evolution of Power

When I walked onto the exhibit floor, I noticed a variety of vehicles. Interested, I approached a booth, and discovered that this was a display of advances in autonomous vehicle technology. What intrigued me was that the focus was not only on the capabilities of the vehicles themselves, but also on the hydrogen-based fuel cells used to power the vehicles.

Public transportation has been a prototype industry for this technology; cities like Palm Springs have adopted the use of electric buses. There are also more than 20,000 forklifts already in use today that are powered by hydrogen fuel cells. That’s far more prolific than expected.

SEMICON West had provided proof that hydrogen fuel cell technology is already at work and developing rapidly. The evolution of the relationship between the automotive and semiconductor industries continues beyond vehicle autonomy and the connected car.

Big Value from Big Data

Inevitably, the conversation around semiconductors turns to Big Data, artificial intelligence and machine learning. It is well-known that these technologies are the biggest drivers of innovation, pushing chips to new levels of performance in memory capacity, logic performance and enabling sensors in electronic applications.

In the semiconductor industry, where Big Data goes, Blockchain is likely to follow and follow quickly. The industry is seeking new ways to integrate Big Data, AI and analytics into products as a vehicle for value creation. We seek insight into everything from the metrological details of a wafer, to the performance statistics of the RF power delivery system for a plasma chamber, to the gaseous and liquid chemicals that are used in a wide variety of process applications.

Blockchain will facilitate tracking the history of every component in the device’s manufacturing journey. In the event of a failure, companies can trace every step of the process and the life cycles of all raw materials to ascertain the root cause, faster and more easily than ever before. With such detailed metrological information, we can reinforce successes and seek out even greater efficiencies.

The most immediate impact for Advanced Energy and our customers will be in data and analytics. Big Data, AI and eventually Blockchain will create a significant paradigm shift in our ability to provide the detailed information our customers want, improve excursion control and pinpoint any latent defects.

Better data acquisition, data integration and analytics will produce more advanced intelligence models for every partner in the value chain. Ultimately, that means better products, higher customer satisfaction, superior risk management, and a distinct competitive advantage.

Semiconductors and In Vivo Patient Monitoring

Semiconductors are not a big-company-exclusive industry. Smaller companies are integrating semiconductor products into niche technologies in medical applications. The developments in recent years are indeed impressive.

One company exhibited a real-time glucose monitoring application that eliminates the need for drawing blood from ones fingers, and relays the data to a smartwatch or smartphone, notifying the user when their blood sugar is outside optimum ranges. This advancement offers a tremendous benefit to the more than 30 million people suffering with diabetes in the U.S.¹ and over 300 million people worldwide.² This is only one of many examples of semiconductor technology being used to aid developments in medical applications.

Semiconductor technology has broadened its horizons in recent years, and the future of the industry has a solid outlook. With the integration into medical devices and services, connectivity and autonomous applications, semiconductors are poised to not only change the game, but change lives.

1. diabetes.org/diabetes-basics/statistics/
2. einstein.yu.edu/centers/diabetes-research/facts-statistics/