The Infosys High Tech practice offers robotic and cognitive automation solutions to enhance design, assembly, testing, and distribution capabilities of printed circuit boards, integrated optics and electronic components manufacturers. We leverage Artificial Intelligence (AI), Robotic Process Automation (RPA), simulation, and virtual reality to augment Manufacturing Execution System (MES) and Manufacturing Operations Management (MOM) systems.

We automate repetitive tasks as well as business processes. Our consultants identify candidate tasks / processes for automation and build proof of concepts based on a prioritization of business challenges and value. It enables chipmakers to address market demand for rugged, high-performance products, while rationalizing production costs. Notably, we adopt open source tools and standardized data protocols to enable advanced automation.

Our cognitive algorithms discover requirements, establish correlations between unstructured / process / event / meta data, and undertake contextual analyses to automate actions, predict outcomes, and support business users in decision-making. Automation, modeling and analysis help semiconductor enterprises achieve improvements in area scaling, material science, and transistor performance. Further, it accelerates design verification, improves wafer yield rates, and boosts productivity at nanometer fabs and assembly test factories.

Infosys Nia, our knowledge-driven chatbot, searches technical manuals and digital content repositories to respond to queries spanning ‘what, when, where, and how’ questions. Associative memory learning and a natural human-machine interface make Infosys Nia a smart virtual assistant. Infosys Nia provides voice-based digital assistance for engineering analytics, customer service, asset maintenance and repair, and technical training.

Our AI researchers, data scientists and IT programmers use knowledge tools and cognitive computing as catalysts for enterprise modernization.


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.


Challenges & Solutions

Machine learning models predict routing congestion, which helps optimize place and route subsystems.

Knowledge-driven automation techniques streamline design verification and minimize retest, while enhancing design and quality.

Incremental learning enables automation systems to ingest new data and improve performance of cognitive models / behavior of chatbots.