Simulating the future for the semiconductor industry

How generative AI is enabling business results at a scale that dwarfs other possibilities.

The semiconductor industry is being rocked by so many waves, it’s hard to identify the tsunamis. Yet, it still might be calm before the storm: Generative AI has the potential to be the most disruptive – and its impact is just beginning.

The wave of AI adoption is reshaping the operational and strategic landscapes of businesses across the spectrum as companies find new ways to optimize processes, enhance decision-making, and innovate products. In fact, 96% of executives admit Generative AI is being discussed in their boardrooms (CRI Report, 2023). AI is proving especially useful for manufacturing and for predicting trends where multiple, complex, data-heavy externalities are in play. We call this Simulated Futures – seeing different variations of the end result. For the chip industry, it’s about to change everything.

We bring you forward-thinking ideas on how semiconductor companies can transform their operations – and their business models – through the simulation capabilities of Generative AI.

Breaking the Iron Triangle through simulation

Many product developers are familiar with the ‘Iron Triangle’ (also known as the Triple Constraint, or Project Management Triangle): given the goals of speed, cost, and quality, you can choose any two, but rarely can you accomplish all three. Now, Generative AI has broken the Iron Triangle. It is enabling semis to bring new, higher-quality products to market swiftly and at lower cost, shaping optimal chip designs and rapidly prototyping, testing, and validating new designs. Gen AI is also enabling more efficient resource allocation and process optimization, driving down production costs.

Generative AI for Software Engineering helps improve efficiency and quality across the whole software life cycle (from design and coding to documentation, testing, deployment, and operations), accelerates the time to market for new software, and reduces the technical debt of enterprises by facilitating large modernization programs of legacy software. It also enables increased security with a reduced attack surface by automatically identifying bugs or vulnerabilities and proposing adjustments to software development teams.

Let’s take a closer look now at production.

Simulated operations: Generative AI on the factory floor

In the realm of network and factory operations, AI serves as a transformative catalyst, optimizing numerous aspects of the production cycle in chip manufacturing, depending on the objective. Generative AI enables an incredible capacity to plant and process modeling. From simulating the right type of chip design to projecting through model-based engineering, the production line of the future can now be re-envisioned through digital twins and fully predict what is indeed required in the physical world to create the highest-efficiency factory floor for semiconductors. Equipment-scale twin helps improve immediately right from design through installation by finding issues before physical build or building equipment expertise faster and more effectively.

As another example, the Generative AI receives an order to create a more sustainable chip design. Through a device-scale twin, detailed visualization of the device helps to reduce cycles of silicon learning, thus reducing waste and resources in production. Similarly, by using process-scale twin, i.e., using simulation to streamline process development, thus reducing chemicals and electricity usage.

As the factory floor becomes AI-trained, the deployment of AI for predictive maintenance is instrumental in foreseeing potential equipment failures and scheduling timely maintenance – sometimes at line speed. A systems failure that might have required a stopped line, a team of engineers, and hours of research, discussion, and testing, can now be solved with no break in production.

Quality assurance systems benefit similarly, only instead of AI anticipating machine irregularities, it targets the detection and rectification of anomalies improving yield. Once again – speed, cost, and quality are all improved.

Simulated supply chains

Intelligent supply chain operations help ensure the availability of necessary materials and components at the right time and at optimal costs – an essential capability in the global climate today. AI can also ensure standards for ethical labor and sustainability in an industry where both are rising in importance. Predictive Demand and Supply Chain Modeling ensures, for example, the right transportation method, timing, and packaging options for various outcomes and predicts what is required from the receivers and other stakeholders in tandem.

For digital products, where the chip is at the core of the functionality, Generative AI enables better processes from a customer experience. Generative AI for Customer Experience enhances customer experience with 4 dedicated generative AI assistants. It allows hyper-personalized customer experience with a synthetic design assistant, elevates customer self-service with personalized chatbots, augments customer care services with a content and knowledge assistant, and boosts sales teams’ performance with a product & offers knowledge assistant.

Change at the core: Generative AI in documentation, HR, and legal departments

AI will impact every industry – some more than others, few more than chip manufacturing. Let’s start deep inside the industry, at some of the back-end processes.

Product reference documentation can run into thousands of precise and detailed technical information. The capability of Generative AI to generate these documents instantaneously from requirements and functional specifications is a game-changer. The ability to cross-check the specification versus the implementation improves accuracy and spares huge downstream costs of customer support. AI cuts this painstaking task from months to moments.

In chip manufacturing firms, the application of AI technologies streamlines multiple functions within internal processes such as HR and Legal.  Generative AI facilitates efficient talent acquisition and management, helping organizations identify optimal candidates with precise qualifications and manage workforce needs. In the transforming world of semiconductor manufacturers, the ability to rapidly identify, attract, and keep talent can make the difference between a new venture succeeding or being scrapped. Additionally, AI aids in ensuring stringent adherence to legal norms across all operations, identifying and addressing potential legal risks and compliance issues, and shielding a company against legal vulnerabilities. These are all part of how Generative AI can be used in an Enterprise setting.

When these tasks are managed well, leadership is free to turn their attention to more innovative, value-adding tasks, such as product innovation and development. There, too, the impact of Gen AI is beginning to make waves.

Looking ahead

Here’s another way to think about the scale of this change, and it speaks to the fundamental nature of Generative AI. Unlike any tool humans have developed, AI has the ability to make decisions. In each of the categories above, we’re going to see a shift from automated to autonomous. But it won’t be groups of people on one side of the building and blinking lights on the other. Teams are going to be integrated – somehow – with people and Gen AI sharing decision-making. How will tasks be divided? Who will take responsibility for successes? For failures? These are some of the questions which we’ll need to address.

And in the core, there is this one capability on its way that truly tests the bounds of credulity: predicting the future. Or, more accurately, simulating more or less likely futures. Today, we have some models that are developed enough to predict bits and pieces of the near future – weather forecasting, for example. What we’re about to see are full-fledged business simulations that leaders will use to inform their decisions. They’ll adapt in real-time and provide decision-makers with practical, probabilistic outcomes. For a cyclical industry with immense dependency on multiple global trends, the ability to reduce uncertainty will change everything.

The accuracy of these simulations will be dependent on the data available to them. For companies that haven’t yet joined collaborative data networks, it may be a good time to get on board.

Zero or one?

The integration of AI across multiple dimensions of the semiconductor industry will bring transformative advancements. By optimizing internal processes, catalyzing product development, and enhancing operational efficiency, Gen AI equips chip manufacturing organizations to navigate the evolving technological landscape with unmatched agility and foresight, opening the door to possibilities previously unimaginable. Today, some leaders are already beginning to benefit. In ten years, there will be two types of semiconductor manufacturers: those that have incorporated Gen AI into their operations and those that exist only in memory.