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Innovations that will help semiconductor manufacturers increase efficiency in real-time

Semiconductor growth is anticipated and there are many tools to help drive it

The global semiconductor industry has experienced many challenges over the past several years amidst a growth in demand for semiconductors in products from earbuds to automobiles. Analysts are now optimistic the industry has begun recovery that will continue into 2024. World Semiconductor Trade Statistics (WSTS) predicts growth of 11.8% in 2024. In the area of AI semiconductor revenue, Gartner anticipates double-digit growth of more than 25%, to $67.1 billion.

Among the many factors that will contribute to the growth will be the ability of semiconductor manufacturers to make the most of several key innovations:
  • Artificial Intelligence: as much as AI is placing demands on chip manufacturers for AI-ready hardware, the semiconductor companies themselves have embraced the potential of AI to improve process quality, optimize production, and increase efficiency in their fabs.
  • Internet of Things: has spurred demand for semiconductors while also being employed by the industry to help keep up with that demand. IoT devices have found their way into the production environment for real-time data capture and monitoring of tools, equipment, and processes. Armed with the right automation solution, these devices facilitate continuous process improvement.
  • Simulated Fabrication: developing new recipes and processes is costly and time consuming. Simulation, or virtual modeling, with AI and ML models enables manufacturers to simulate the process flow. They can generate a high volume of data in days instead of in the weeks or months it would take to produce enough wafers to gather this data. The models can quickly provide insights into bottlenecks, cycle time and output, as well as predict problems that may impact products—all without having to interrupt current production.
  • Innovative Technologies: increasingly smaller chips necessitate greater accuracy in placement of both patterns and wiring. Manufacturers have turned to advances in fabrication technology such as robotic wafer handling and unique fabrication techniques such as additive manufacturing.
  • Innovative Materials: semiconductor companies have looked to materials such as gallium nitride (GaN) and silicon carbide (SiC), to achieve higher operating temperatures, high voltage resistance, a smaller form factor, and faster switching. Manufacturers’ continued ingenuity in working with innovative materials will help them overcome chip size limitations.
Semiconductor manufacturers who are willing to leverage the power of innovations that can improve their process, productivity, and quality in factories of any size will be best positioned to meet customer requirements and stay relevant in this dynamic marketplace.

The global semiconductor industry has experienced many challenges over the past several years amidst a growth in demand for semiconductors in products from earbuds to automobiles. Analysts are now optimistic that the industry has begun recovery that will continue into 2024. World Semiconductor Trade Statistics (WSTS) predicts growth of 11.8% in 2024. In the area of AI semiconductor revenue, Gartner anticipates double-digit growth of more than 25%, to $67.1 billion.

Among the many factors that will contribute to the growth will be the ability of semiconductor manufacturers to make the most of several key innovations.

Artificial Intelligence: as much as AI is placing demands on chip manufacturers for AI-ready hardware, the semiconductor companies themselves have embraced the potential of AI to improve process quality, optimize production, and increase efficiency in their fabs.

Internet of Things: has spurred demand for semiconductors while also being employed by the industry to help keep up with that demand. IoT devices have found their way into the production environment for real-time data capture and monitoring of tools, equipment, and processes. Armed with the right automation solution, these devices facilitate continuous process improvement in smart manufacturing in the semiconductor industry.

Simulated Fabrication: developing new recipes and processes is costly and time-consuming. Simulation, or virtual modeling, with AI and ML models enables manufacturers to simulate the process flow. They can generate a high volume of data in days instead of in the weeks or months it would take to produce enough wafers to gather this data. The models can quickly provide insights into bottlenecks, cycle time, and output, as well as predict problems that may impact products—all without having to interrupt current production.

Innovative Technologies: increasingly smaller chips necessitate greater accuracy in placement of both patterns and wiring. Manufacturers have turned to advances in fabrication technology such as robotic wafer handling and unique fabrication techniques such as additive manufacturing.

Innovative Materials: semiconductor companies have looked to materials such as gallium nitride (GaN) and silicon carbide (SiC), to achieve higher operating temperatures, high voltage resistance, a smaller form factor, and faster switching. Manufacturers’ continued ingenuity in working with innovative materials will help them overcome chip size limitations.

Conclusion

Semiconductor manufacturers who are willing to leverage the power of innovative semiconductor solutions that can improve their process, productivity, and quality in factories of any size will be best positioned to meet customer requirements and stay relevant in this dynamic marketplace.

FAQs

Why is data preparation for AI considered a challenge in semiconductor manufacturing?

Data preparation for AI can be expensive in terms of time and resources, making it a barrier, especially for smaller companies. Historical data may also be insufficient due to evolving environments.

Simulation allows for the creation of synthetic data, eliminating the need for extensive data cleaning. It provides an efficient way to generate diverse and high-quality data for AI training.

Simulation enables the exploration of AI in essential use cases without resource limitations. It accelerates projects, reduces costs, and quantifies the impact of changes before implementation, reducing risks.

Simulation plays a crucial role in RL by providing a detailed environment for agents to learn and make decisions. In ML, it allows models to be trained on rich datasets, leading to operational efficiency gains and KPI comparisons.

Accelerate the value AI can bring to your factory’s operations with simulation! Simulation addresses insurmountable data preparation tasks when deploying AI in a production environment. Deploying AI in production is no small feat; there are many steps to a successful implementation. An AI lifecycle consists of design, development, and deployment components (as shown in figure 1). While each phase has its own challenges, preparing the substantial, high-quality data required for an accurate model during the first stage often is enough to discourage companies from continuing the process toward deploying AI.
Figure 1: AI end-to-end lifecycle.
Figure 1: AI end-to-end lifecycle.

Collecting and formatting diverse data required for deep learning is often expensive in both time and money, which is sometimes infeasible for smaller companies. Even if a manufacturer has the resources required to collect large amounts of data, historical data is often inadequate due to an evolving environment. For example, tools and process steps are constantly adapting to uncertainties found in supply chains, labor limitations, or change in part types. Evolving scenarios (i.e., adding more time constraint steps) in semiconductor manufacturing are especially common as technology nodes progress. Consequently, these rapid changes do not allow time for a diverse, historical dataset to develop to train models.

However, what if there was a way to get more quality data? What if AI could be explored on business essential use cases without the resource limitations of technology advancements? With simulation, you can answer these ‘what-ifs’! You can model various scenarios and generate synthetic data to use in AI training. Projects can be accelerated without the cost of cleaning raw datasets and in significantly less time than it takes to collect a sufficient amount of data. Quantifying the impact of changes in a simulated environment prior to production implementation is also key to avoiding unnecessary, costly risks. Furthermore, training models on rich datasets develop more robust, resilient models. Evaluating on-edge cases or other diverse scenarios that seldom occur in historical data increases generalization abilities of models, which improves accuracy overall.

Requirements for an acceptable simulation model

Simulation requires many details for an accurate semiconductor manufacturing replication. Not only must simulations replicate various scenarios in a semiconductor factory environment, but they also need to have the ability to replicate dispatching and scheduling rule behavior found in a production system. Quick, scalable run times and flexibility to accommodate various planning horizons are also necessary. From simulating a short-term planning situation (i.e., two-day run to illustrate a tool down scenario) to a larger simulation model to represent a long-term planning example (i.e., one year run to demonstrate the impact of adding new equipment), end users will always want a reasonable run time with results in minutes.

Use cases: where simulation can support AI in smart manufacturing

There are multiple scenarios where simulation plays a key role. For starters, Reinforcement Learning (RL) is growing in popularity and simulation can play a vital role in this architecture. RL involves an agent, a computer program or an intelligent system, attempting to take actions in an environment to change its state for maximum manufacturing and productivity. Here, an accurate, very detailed simulation model can act as the environment where the agent’s actions can be observed and changed. For example, a simulated environment can support an agent in learning when to release lots in a queue-time constraint scenario. Figure 2 below shows an RL framework that includes a simulator environment.
Figure 2: RL architecture with simulation.
Figure 2: RL architecture with simulation.
Another great example is utilizing simulation in a machine learning (ML) framework, such as predicting cycle time. Simulation allows models to be trained on a rich, multi-year dataset, which aids prediction accuracy. Operational efficiency gains are then possible as planners are given the opportunity, in a non-production, simulated environment, to test and validate changes such as updating dispatching and scheduling parameters required for late lot predictions. Just as in the RL example above, simulation can find key performance indicator (KPI) differences by evaluating ML or RL models versus existing dispatching rules or scheduling models. This provides the powerful opportunity to compare those differences.
Together, simulation and AI can create a productivity solution that enables real operational efficiency gains. A fast, flexible, scalable, and very accurate simulation aspect provides the opportunity to support AI solutions for current dispatching and scheduling dilemmas. From predicting lot cycle time to optimizing dispatching parameter values and scheduling constraints, SmartFactory AI Productivity is rapidly accelerating AI innovations into a reality!

About the Author

Picture of SmartFactory Automation Solution Experts Team
SmartFactory Automation Solution Experts Team
The Applied SmartFactory® automation solution experts team develops integrated automation solutions for semiconductor manufacturers to improve the performance of factories. From implementing a manufacturing execution system that advances collaboration and automation, to integrating AI/ML technologies for faster decision making, SmartFactory automation solutions enable manufacturers to prioritize quality and reliability across every stage of the manufacturing process.