Zerodrift

Zerodrift is a term used in engineering to describe approaches, devices, or algorithms that aim to minimize or eliminate drift in a measurement system over time. Drift refers to slow, systematic changes in a sensor’s baseline or offset, which can degrade accuracy even when the true value remains constant. Zerodrift encompasses hardware solutions, such as zero-drift or self-calibrating sensors and zero-drift amplifiers, as well as software strategies that continuously estimate and compensate for bias.

In hardware, methods include reference stabilization, temperature compensation, and auto-calibration techniques that reduce offset drift, such as chopper stabilization. In software, drift can be mitigated by periodic calibration against known references, adaptive bias estimation, Kalman filtering, and online drift tracking. Hybrid approaches combine sensor data fusion with continuous calibration to maintain accuracy in changing environments.

Applications span precision metrology, industrial instrumentation, aerospace and automotive sensing, medical devices, and robotics, where long-term stability is critical. Zerodrift is particularly valued in low-drift and high-accuracy contexts, as it reduces the need for frequent recalibration.

Evaluation typically uses metrics such as bias stability, drift rate (offset change over time), and Allan deviation, assessed under defined environmental conditions. Limitations include added cost, complexity, and potential trade-offs with noise performance or response time.

See also drift, bias stability, chopper stabilization, self-calibrating sensors, zero-drift op-amps.