The Infosys High Tech practice leverages a suite of analytical and modeling tools to capture data, understand the context from a connected manufacturing ecosystem, and distill predictive insights for operational excellence. Our web-based solutions allow manufacturers of industrial controllers, embedded industrial and consumer end devices, and on-chip products to convert product and enterprise data into predictive insights.
We combine Artificial Intelligence (AI) with Industry 4.0 principles to create a smart factory environment for manufacturing electronic components and devices. Automated processes accelerate design cycles, ensure steady material flow, and rationalize asset maintenance. In addition, it provides flexibility to configure customized testing modules for superior quality control. Significantly, it supports advanced recipe control and management methodologies for mass production.
Our data frameworks integrate diverse formats and sources to create a unified database that can be accessed by analytical tools, machine learning models and mobile devices. We adopt traditional industrial automation protocols and open source tools for secure data transfer between operations, supply chain, and third-party systems. Our experts have rich experience across web services standards and communications protocols for automation technology, including, Modbus, PROFIBUS and OPC Unified Architecture (UA).
The Infosys digital ecosystem ensures compliance with standards of the Semiconductor Equipment and Materials International (SEMI) consortium for seamless communication between applications, inspection tools, and process systems for Run-to-Run (R2R) control, Fault Detection and Classification (FDC), fault prediction, and process control. Further, our data science-driven predictive diagnostics tools facilitate root cause analysis of engineering systems to streamline tool startup and commissioning.
Success story: Online marketing platform grows global business
State-of-the-art technology infrastructure for end-to-end marketing services improved customer satisfaction score by 25% at a semiconductor chip manufacturing company.
Near real-time data visibility helps control process parameters and predict variability to eliminate deviations in the amount of deposit on each wafer, thereby ensuring consistent wafer surface.
AI-driven analytical techniques apply learning from historical operations and wafer scrap data to better manage recipe settings and processes.
Model-based control enables root cause analysis of operational issues to eliminate bottlenecks, and improve fab throughput as well as tool utilization.