Digital Twin — A real-time virtual replica of a physical device, system, or process that enables simulation, monitoring, and predictive analytics throughout the product lifecycle.
Digital Twin
A Digital Twin is a dynamic virtual representation of a physical object, system, or process that is continuously synchronized with its real-world counterpart through sensor data, IoT connectivity, and computational models. It enables engineers to simulate behavior, predict failures, optimize performance, and test changes — all without touching the physical asset.
How Digital Twins Work
A digital twin consists of three core components:
| Component | Description | Example |
|---|
| Physical entity | The real-world object or system | Industrial robot, wind turbine, PCB assembly line |
| Virtual model | The digital replica (CAD, physics simulation, ML model) | 3D model with thermal/stress simulation |
| Data bridge | IoT sensors + connectivity linking physical to virtual | Temperature, vibration, current sensors via MQTT/OPC UA |
The data flows bidirectionally:
- Physical → Virtual: Sensor telemetry updates the model in real time.
- Virtual → Physical: Simulation results trigger control actions (e.g., adjust motor speed, schedule maintenance).
Types of Digital Twins
| Type | Scope | Use Case |
|---|
| Component Twin | Single part or sensor | Bearing wear prediction, chip thermal model |
| Asset Twin | Complete device or machine | Industrial robot monitoring, FPGA system diagnostics |
| System Twin | Interconnected systems | Factory production line optimization |
| Process Twin | End-to-end workflow | Supply chain logistics, quality control process |
Digital Twin Maturity Levels
| Level | Capability | Description |
|---|
| Level 1 — Descriptive | Monitoring | Visualize current state from sensor data |
| Level 2 — Diagnostic | Root-cause analysis | Correlate anomalies with operating conditions |
| Level 3 — Predictive | Forecasting | ML-based failure prediction, remaining useful life |
| Level 4 — Prescriptive | Autonomous optimization | Automatically adjust parameters for optimal performance |
| Level 5 — Autonomous | Self-healing | Detect, diagnose, and resolve issues without human intervention |
Digital Twins for Hardware Products
For embedded systems and hardware manufacturers, digital twins offer unique value:
During Design
- Virtual prototyping — Test thermal, mechanical, and electrical behavior before fabricating physical prototypes.
- Design space exploration — Evaluate thousands of configurations (component placement, stack-up variations) in simulation.
- Signal/power integrity prediction — Simulate PCB performance under real operating conditions.
During Manufacturing
- Process optimization — Monitor and optimize SMT pick-and-place, reflow soldering, and test processes.
- Quality prediction — ML models predict defects before final test, reducing scrap rates.
- Yield improvement — Correlate process parameters with test outcomes across production batches.
During Operation (Field)
- Predictive maintenance — Forecast component failures using vibration, temperature, and current sensor data.
- Over-the-air optimization — Update device firmware based on field performance insights.
- Fleet management — Monitor and compare thousands of deployed IoT devices simultaneously.
Technology Stack
| Layer | Technologies |
|---|
| Edge computing | FPGA/SoC-based sensor fusion, real-time preprocessing |
| Connectivity | MQTT, OPC UA, AMQP, LoRaWAN, 5G |
| Data platform | Time-series databases (InfluxDB, TimescaleDB), data lakes |
| Modeling | FEM (ANSYS, COMSOL), CFD, system-level simulation (Simulink) |
| AI/ML | TensorFlow Lite, ONNX Runtime, anomaly detection models |
| Visualization | 3D web (Three.js, Unity), dashboards (Grafana) |
Digital Twin Standards
| Standard | Organization | Focus |
|---|
| ISO 23247 | ISO | Digital twin framework for manufacturing |
| IEC 63278 (AAS) | IEC / IDTA | Asset Administration Shell — standardized digital twin interface |
| Digital Twin Definition Language (DTDL) | Microsoft / Azure | IoT digital twin modeling language |
| W3C Web of Things (WoT) | W3C | Interoperable thing descriptions |
- IoT — Sensor networks that feed data to digital twins.
- Edge AI — On-device intelligence enabling local digital twin processing.
- SoC — System-on-Chip devices powering edge-based digital twin computation.
