Semiconductor Digital Twin Engineer

AI, Simulation & Smart Manufacturing

Location: Bengaluru / Hyderabad / Ahmedabad / Noida/ Pune , India

Work Mode: Onsite

Experience: 6 15 Years

Industry: Semiconductor Manufacturing | Foundry | OSAT | AI | Industry 4.0

Role Overview

We are seeking an experienced Semiconductor Digital Twin Engineer to design and deploy digital twin solutions for semiconductor fabs, OSAT facilities, equipment, process modules, utilities, cleanrooms, and advanced manufacturing operations.

The role combines semiconductor process knowledge, industrial data, simulation, AI/ML, IoT, manufacturing systems, and systems engineering to create virtual representations of physical assets and production environments. These digital twins will support process optimization, predictive maintenance, capacity planning, yield improvement, equipment performance, factory simulation, and real-time operational decision-making.

The successful candidate will work closely with Process, Equipment, Manufacturing, Facilities, Yield, Automation, Data Engineering, AI/ML, MES, and Digital Transformation teams.

Key Responsibilities

• Design digital twin architectures for semiconductor fabs, OSAT plants, equipment, process modules, utilities, and production lines.
• Develop virtual models of manufacturing processes, equipment behavior, material flow, cycle time, and factory operations.
• Integrate real-time and historical data from MES, equipment sensors, SPC, FDC, APC, metrology, yield, maintenance, and facility systems.
• Build physics-based, data-driven, and hybrid simulation models for manufacturing optimization.
• Develop AI/ML models for predictive maintenance, process prediction, anomaly detection, and operational optimization.
• Simulate production scenarios to evaluate capacity, throughput, bottlenecks, scheduling, and tool utilization.
• Support digital twins for lithography, etch, deposition, CMP, diffusion, implant, metrology, packaging, assembly, and test operations.
• Create equipment health and remaining useful life models to improve uptime and maintenance planning.
• Model fab utilities and cleanroom systems, including UPW, gases, chemicals, HVAC, vacuum, power, and environmental controls.
• Collaborate with automation and MES teams to connect digital twins with live manufacturing workflows.
• Develop dashboards and visualization environments for engineers, operators, and factory leadership.
• Validate digital twin outputs against production data and continuously improve model accuracy.
• Support scenario planning, virtual commissioning, process qualification, and new factory ramp-up.
• Establish reusable methodologies, data standards, documentation, and governance for digital twin deployment.

Required Qualifications

• Bachelor s or Master s degree in Electronics, Electrical Engineering, Mechanical Engineering, Chemical Engineering, Industrial Engineering, Computer Science, Data Science, Automation, or a related field.
• 6 15 years of experience in semiconductor manufacturing, industrial simulation, digital twins, factory automation, equipment engineering, or manufacturing analytics.
• Strong understanding of semiconductor manufacturing processes and factory operations.
• Experience integrating industrial data with simulation, analytics, or AI platforms.
• Hands-on experience developing or deploying digital twin, discrete-event simulation, or industrial modeling solutions.

Technical Skills Digital Twin & Simulation

• Digital Twin Architecture
• Physics-Based Modeling
• Data-Driven Modeling
• Hybrid Modeling
• Discrete-Event Simulation
• System Simulation
• Factory Simulation
• Equipment Simulation
• Virtual Commissioning
• Scenario Analysis
• What-If Simulation

Semiconductor Manufacturing

• Wafer Fabrication
• Process Integration
• Lithography
• Etch
• CVD / PVD / ALD
• CMP
• Diffusion
• Ion Implantation
• Metrology
• Yield Engineering
• Advanced Packaging
• Assembly and Test
• Fab Facilities

Manufacturing Systems

• Manufacturing Execution Systems (MES)
• Statistical Process Control (SPC)
• Fault Detection and Classification (FDC)
• Advanced Process Control (APC)
• Equipment Data Collection
• Computerized Maintenance Management Systems (CMMS)
• Overall Equipment Effectiveness (OEE)
• Factory Scheduling
• Capacity Planning

AI, Analytics & Programming

• Python
• SQL
• Machine Learning
• Time-Series Analytics
• Predictive Maintenance
• Anomaly Detection
• Optimization Algorithms
• Reinforcement Learning
• Pandas
• NumPy
• Scikit-learn
• TensorFlow / PyTorch

Simulation & Engineering Platforms

• Siemens Tecnomatix
• AnyLogic
• FlexSim
• MATLAB / Simulink
• Ansys Twin Builder
• Dassault Syst mes DELMIA
• NVIDIA Omniverse
• Unity or Unreal Engine
• Modelica / Dymola
• COMSOL Multiphysics

Industrial Connectivity & Data

• SECS/GEM
• OPC-UA
• MQTT
• Industrial IoT
• Sensor Data
• Edge Computing
• Kafka
• Spark
• Databricks
• Time-Series Databases

Cloud & Deployment

• AWS
• Microsoft Azure
• Google Cloud Platform
• Docker
• Kubernetes
• MLflow
• CI/CD
• REST APIs