Beyond Moore’s Law: How AI is Enabling the Next Generation of Chips

The global semiconductor market is projected to reach a staggering $1 trillion by 2030. This growth is fueled by the insatiable demand for faster, more powerful chips driving everything from smartphones to artificial intelligence.

Dominated by the communication industry in the usage of these semiconductors, the APAC region has a market share of more than 67% heading towards monopoly. To counter this, the countries around the globe are trying to change their regulatory landscape to make their markets more investment friendly by bringing in their own versions of Chips Acts to increase their share of the market.

The Need for Disruption: Challenges and Opportunities

Several challenges threaten to impede progress of the semiconductor value chain:
• Moore’s Law Slowdown: The long-held principle that transistor density on a chip doubles roughly every two years is reaching its physical limits. New approaches are needed to maintain performance gains.
• Yield Optimization: Manufacturing complex chips with minimal defects is crucial. Traditional methods struggle to account for the growing number of variables involved.
• Design Complexity: Modern chip design involves billions of transistors and intricate interconnections. Manual design processes are becoming increasingly time-consuming and error prone.

AI as the Disruptive Force

The industry is facing a period of intense competition and ever-shrinking chip sizes. To stay ahead, companies must embrace positive disruption — transformative change that creates new opportunities. Artificial intelligence (AI) presents a powerful tool for achieving this, optimizing processes, accelerating innovation, and propelling the industry forward. The global AI Chip Market which reported a revenue of $20 billion in 2022, is expected to grow 30% to reach $165 billion by 2030.

AI offers a path to overcome these challenges and unlock new possibilities. Here’s how:

• Optimizing Chip Design: AI algorithms can analyze vast datasets of design parameters, material properties, and performance characteristics. This allows them to suggest optimal layouts, minimizing power consumption, maximizing performance, and reducing the risk of errors. A study by McKinsey found that AI-powered design tools can reduce design cycles by up to 20%.

• Predictive Maintenance: Semiconductor fabrication involves complex equipment susceptible to breakdowns. AI can analyze sensor data from machines, predict potential failures, and trigger preventative maintenance. This reduces downtime, improves production efficiency, and minimizes costly repairs. A report by Gartner predicts that AI-powered predictive maintenance can save manufacturers up to 10% on maintenance costs.

• Yield Management: AI can analyze real-time manufacturing data to identify process variations that could lead to defects. By proactively adjusting parameters, AI can significantly improve yield rates, leading to cost savings and higher production volumes. A study by Accenture found that AI-driven yield management can increase wafer yields by up to 5%.

• Material Innovation: Discovering new materials with superior properties is critical for future chip development. AI can analyze vast chemical databases and simulate material behavior at the atomic level. This accelerates the discovery of new materials with desired characteristics, paving the way for next-generation chips.

• Digital Twin: Creating a virtual representation of the chip under manufacturing to efficiently explore, analyze and test the chip before being finalized and sent for manufacturing. With the challenge of time-to-market for semiconductor companies, digital twins are expected to save time, cost, and effort especially in the research and development phase of the supply chain.

Real-World Scenarios of AI in Action

Several leading semiconductor companies are already reaping the benefits of AI:
• Intel: Utilizes AI to optimize chip layouts and reduce power consumption in its processors.
• Samsung: Leverages AI for predictive maintenance in its fabrication plants.
• TSMC: Employs AI to analyze chip designs and identify potential yield issues before manufacturing.

Recommendations for the Future

As the industry embraces AI, here are some key recommendations for semiconductor companies:
• Invest in AI Talent: Building an in-house team of data scientists, AI engineers, and domain experts familiar with semiconductor processes is crucial.
• Develop a Robust AI Strategy: Clearly define goals for AI implementation, identify areas for initial projects, and establish metrics to measure success.
• Prioritize Data Infrastructure: Secure and manage vast amounts of data from design tools, manufacturing processes, and testing equipment. This data is the fuel for AI algorithms.
• Embrace Collaboration: Partner with AI research labs or startups specializing in AI for chip design and manufacturing.
• Focus on Explainable AI: Ensure that AI-driven decisions are transparent and interpretable by human engineers for trust and responsible development.

Conclusion: A Brighter Future with AI

AI is poised to revolutionize the semiconductor industry, ushering in an era of faster, more efficient, and more cost-effective chip development. By embracing AI and implementing the recommendations outlined above, semiconductor companies can overcome current bottlenecks, unlock new avenues for innovation, and secure their competitive advantage in the years to come.
This positive disruption will not only benefit the industry but also empower the development of groundbreaking technologies across sectors, shaping a future driven by smarter and more powerful chips.

References:

[1] “Global Semiconductor Market Size Reached $555.9 Billion in 2023,” IC Insights, https://en.ctimes.com.tw/DispNews.asp?O=HK62EDP7PXWSAA00NZ

[2] R. R. Schaller, “Moore’s law: past, present and future,” in IEEE Spectrum, vol. 34, no. 6, pp. 52–59, June 1997, doi: 10.1109/6.591665.

[3] McKinsey & Company. (2020). Empowering innovation through AI in chip design. [Report] https://www.mckinsey.com/industries/semiconductors/our-insights/artificial-intelligence-hardware-new-opportunities-for-semiconductor-companies

[4] Gartner Press Release. (2020, August 25). Gartner predicts that by 2022, AI will drive 10% of supply chain maintenance costs. [Press release] https://www.gartner.com/en/newsroom/press-releases/2024-02-20-gartner-says-top-supply-chain-organizations-are-using-ai-to-optimize-processes-at-more-than-twice-the-rate-of-low-performing-peers

[5] Accenture. (2020). Why AI matters in high tech [Report]. https://www.accenture.com/us-en/insights/artificial-intelligence-summary-index