5 ways manufacturers can benefit from AI in chip design

Artificial intelligence (AI) in chip design is one of the most promising applications of the technology in manufacturing. It promises to manufacture chips faster and more accurately, while reducing pressure on the workforce.

AI is quickly becoming one of the most versatile and practical tools available to manufacturers. As electronics manufacturers face increasing demand and supply chain pressures, the application of AI in chip design is gaining momentum.
Chip designers face the daunting task of providing increasing functionality while shrinking in size, while managing production and final costs. AI that excels at analyzing and balancing multiple complex factors is an ideal solution. Here are five ways manufacturers can use AI to optimize chip designs.

Accelerate chip design cycles
Improving efficiency is one of the biggest advantages of AI in chip design. Optimizing the design of a chip means calculating and balancing thousands of possibilities, from materials to component layouts to node types. For human engineers, this process is slow and laborious, but AI models can weigh these factors to find the ideal balance in a very short time. AI can generate the ideal chip floor plan in less than six hours, whereas it would take a human research team months to achieve the same results. Even if engineers have to make further modifications to the design, they have a head start of months.
Manufacturers can significantly reduce lead times by simplifying the initial design phase. They can bring new chips to market faster, resulting in a better return on investment.

Reduce production costs
The use of AI in chip design also allows manufacturers to produce components at a lower cost. Most of these savings come from the speed of artificial intelligence. With R&D times reduced from months to weeks or even days, manufacturers spend far less on personnel and machine costs throughout the production lifecycle.
This flexibility also allows manufacturers to take advantage of more cost-effective chip technologies. Application-specific integrated circuits (ASICs) cost less to produce than more traditional general-purpose chips, but the initial engineering costs are higher. However, if chipmakers use AI to simplify early development, ASICs are a more viable option, allowing them to maximize cost-effectiveness.
AI can also consider cost-effectiveness when designing chips. Smart models can take into account material costs and complexity when comparing design possibilities, giving manufacturers a less expensive option for production. Theoretically, human experts could also find these possibilities, but this takes longer and is less reliable.
Improve chip performance

Performance is another key consideration in chip design. The demand for device functionality is on the rise, but supporting high-end features while maintaining a reasonable cost is often a challenge for engineers. Because AI can compare thousands of parameters faster than humans, it can better manage these complex considerations.
In addition, the use of AI-aided design software allows manufacturers to discover design choices that can improve chip performance that they might otherwise miss.
As manufacturers use machine learning in more chip designs, these algorithms will collect more real-world data, becoming more effective over time. Therefore, wider adoption of AI will lead to continued and exponential improvements in chip design.

Improve workmanship
AI in chip design can also be applied to processes beyond the design phase. Predictive models can use design and data about production lines in manufacturing facilities to analyze what the manufacturing process will look like. They can then recommend design or workflow adjustments to make the product easier to produce.
Some chip designs may look ideal on paper, but in practice introduce too many production complications. A prototype may require five-axis machining, but this requires specially trained operators, increasing the risk of human error. AI can notice this risk and then suggest alternative designs that manufacturers could use simpler methods.
These factors are easy to overlook for humans, but they have far-reaching consequences. AI-guided design helps electronics companies solve these manufacturing problems to minimize lead times and production costs on production lines.

Alleviating talent shortages
As the demand for electronics increases, many companies find themselves facing a shortage of skilled talent. Applying AI to chip designs can close these gaps by allowing smaller teams to do more in less time.
By 2030, U.S. semiconductor companies may face a talent shortage of 300,000 engineers and 90,000 technicians. Reskilling and upskilling are key steps to address this gap, but in reality, many businesses will still face significant workforce pressure. However, if the chip design is not as long or complex, the impact of this shortage will be less.
Since AI greatly simplifies the design process, it gives workers more time to complete other tasks. Similarly, the automation of more processes means that less skilled and experienced employees can still perform chip design duties. Therefore, the technical talent gap will not affect the company much.
Artificial intelligence in chip design is a relatively new but increasingly valuable solution. These automation tools are essential to keep the industry competitive. Applying AI to solve these five areas will ensure that electronics manufacturers adapt to the growing challenges.