Introducing AI and big data will only improve their performance and profitability since AI will enable semiconductor businesses to capture overall value from the technology stack.
FREMONT, CA: Businesses claim that COVID-19 has sped up the semiconductor industry's digital transformation. Using AI/ML use cases contributes to improving semiconductor manufacturing efficiencies. The semiconductor sector is home to a wide variety of AI applications. Semiconductor makers house hundreds of tools, each producing gigabytes of data. It is impossible to search for anomalies in this data. As a result, AI is helpful in these cases for assessing this data and spotting even the most minor functional irregularities. To compete, semiconductor companies must be faster and more agile than ever. Here is a look at the semiconductor industry's expanding use of AI:
Chip design and development
One of the significant issues in the semiconductor supply chain is the processing time for chip manufacturing. Production costs are lost during this time due to testing and yield losses. As a result, including AI applications into the production cycle enables organizations to systematically examine failures at each stage of production, assisting manufacturers in optimizing operations. This capacity to estimate losses becomes even more essential when working with next-generation semiconductor materials, which are more expensive (and volatile) than standard silicon.
Wafer visual inspection
Visual inspection of wafers aids in quality assurance by discovering faults early in the front-end and back-end manufacturing processes. Developments in deep learning technologies for computer vision enable wafer-inspection systems to automatically find and classify flaws at rates comparable to or better than human inspections. This method assists businesses in gaining early insights into potential process or tool variations, allowing them to discover problems earlier and enhance yields while lowering costs.
AI is implemented in at least one business function. AI usage is vital in product or service development and service operations functions. Within these activities, most organizations have reported revenue increases for inventory and parts optimization, pricing and marketing, customer-service analytics, and sales and demand forecasting. AI facilitates physical operations like shifting and tracking things and more complex procedures like error-free planning and demand forecasting.
Material planning and procurement
Unsurprisingly, the procurement function has frequently been a late adoption of modern technologies. Using AI algorithms to track supply and demand and evaluate manufacturing data gives the procurement team actionable information to satisfy market demands. Integrating the procurement function with other systems for determining spend analytics improves the manufacturers' planning cycle, increases supply chain resilience, and paves the road to unlocking latent efficiency and savings possibilities.