Digital twins promise to mirror every move on the factory floor, from production flows to equipment health. But a twin is only as good as the quality of the data that feeds it.
Here, Ross Turnbull, Director of Business Development at Application Specific Integrated Circuit (ASIC) design and supply specialist Swindon Silicon Systems, explains why without precise, reliable signals, even the smartest algorithms cannot make a twin identical to reality.
Across industry, digital twins have moved from C-suite concept to operational infrastructure. By 2023, “29 per cent of global manufacturing companies have either fully or partially implemented their digital twin strategies” using them to optimise throughput, accelerate production cycles and stabilise quality while simulating operational changes before making physical adjustments.
The ambition is clear: create a digital representation of physical assets that is accurate enough to support real-time decisions. But digital twins live or die on data quality at the edge.
Data you can trust
Every digital twin begins with the sensor. Physical inputs such as vibration, torque, pressure and temperature must be captured, conditioned and converted into digital signals before algorithms can interpret them. Signals that are noisy, misaligned or unstable do not cause dramatic failure; they gradually erode confidence, delay predictions and distort correlations.
As twins move into real-time control loops, the demands intensify. Vision systems guide robots dynamically rather than merely inspecting outputs. Production lines adjust parameters in response to torque signatures or harmonic distortion. Maintenance systems rely on tightly correlated vibration and current measurements to predict failure before downtime occurs. In all these applications, high-resolution signals from multiple inputs must be captured simultaneously, aligned precisely in time and sampled reliably if digital models are to reflect physical behaviour accurately.
But most general-purpose processors were never designed for this level of real-time processing. They evolved to execute discrete logic and monitor relatively low-frequency status signals, not to manage tightly correlated analogue inputs across microsecond timescales with sustained stability over years of continuous operation.
The importance of hardware
The critical inflection point for digital twins lies at the analogue-to-digital boundary where physical behaviour becomes usable data. If the signal layer is poorly engineered, latency accumulates, noise distorts readings and drift undermines long-term stability.
As sensor density rises, robotic joints stream encoders, Hall sensors and inertial data while process equipment outputs pressure, flow and vibration signals, tight synchronisation is non-negotiable. Without reliable data acquisition and synchronisation, even sophisticated twins struggle to mirror reality.
This is where mixed-signal ASICs move from component choice to system architecture. By integrating the signal chain, from conditioning and amplification through to filtering and conversion, onto a single device, an ASIC enables each stage to be tuned to the application’s bandwidth, dynamic range and correlation requirements rather than forcing the process to conform to a generic converter. The result is lower latency, tighter timing and greater long-term stability at the point of capture, which preserves accuracy upstream in the twin.
Integration also compounds practical advantages on the factory floor. Consolidating functions reduces PCB footprint and the bill of materials, improves mechanical robustness and can enhance thermal behaviour and EMC performance by shortening sensitive analogue paths.
Precision creates identity
The impact of reliable data acquisition is visible across several manufacturing environments. Automotive production lines that analyse vibration and current signals detect bearing wear earlier and with greater confidence. Continuous processing plants can distinguish hydraulic disturbances from mechanical degradation, and precision assembly lines maintain yield stability through aligned thermal and electrical signals.
In each case, the gains stem from ensuring that signals entering analytical models are stable and accurate, not from layering additional algorithms onto imperfect data. As analytics migrate toward the edge and embedded AI becomes commonplace, the need for reliable, low-latency data acquisition only increases and consequently, hardware and software must be designed together.
The evolution of digital twins from descriptive tools to operational decision engines has made one thing clear: accuracy is not created in the cloud, but is preserved at the point of capture.
Manufacturers investing heavily in digital transformation must therefore treat the signal layer as a priority. Signal integrity, synchronisation, drift management, latency control and lifecycle stability are foundational to delivering actionable, reliable digital twins.
A digital twin cannot be identical if its signals are approximate. To make twins truly identical, manufacturers must put precision where it belongs: at the source, in the silicon that captures reality before it even becomes data.
To discuss how an ASIC could support your digital twin strategy by enabling highly reliable data acquisition, get in touch with Swindon Silicon here.
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