For a long time wet bulk-micromachining has been an easy and cost-effective method for fabricating silicon micro-sensors. Anisotropic wet etching is the key processing step for the fabrication of microstructures. Among different silicon etchants, TMAH based etchants are becoming popular because of their low toxicity and CMOS compatibility. The etch rate of wet anisotropic etching of silicon depends on the crystal plane orientation, type of etchant and their concentrations. In anisotropic etching, convex corners are attacked; therefore, a proper compensating structure design is often required when fabricating microstructures with sharp corners (convex corners). In the present work, two ⟨1 0 0⟩ bar compensation structures have been used for convex corner compensation with 25% wt TMAH–water solution at 90 ± 1 °C temperature. Generalized empirical formulae are also presented for these compensation structures for TMAH–water solution. Both the ⟨1 0 0⟩ bar structures provide perfect convex corners but the ⟨1 0 0⟩ wide bar (structure 2) is more space efficient than the ⟨1 0 0⟩ thin bar (structure 1) and it requires nearly 30% less groove width.

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Summary The paper presents modeling and simulation of ion‐sensitive field‐effect transistor (ISFET)‐based pH sensor with temperature‐dependent behavioral macromodel and proposes to compensate the temperature drift in the sensor using intelligent machine learning (ML) models. The macromodel is built using SPICE by introducing electrochemical parameters in a metal‐oxide‐semiconductor field‐effect transistor (MOSFET) model to simulate ISFET characteristics. We account for the temperature dependence of electrochemical and semiconductor parameters in our macromodel to increase its robustness. The macromodel is then exported as a subcircuit element, which is used to design the readout interface circuit. A simple constant‐voltage, constant‐current (CVCC) topology is utilized to generate the data for temperature drift in ISFET pH sensor, which is used to train and test state‐of‐the‐art ML‐based regression models in order to compensate the drift behavior. The experimental results demonstrate that the random forest (RF) technique achieves the best performance with very high correlation and low error rate. Corresponding curves for output signal using the trained models show highly temperature‐independent characteristics when tested for pH 2, 4, 7, 10, and 12, and we obtained a root mean squared error (RMS) variation of ΔpH ≤ 0.024 over a temperature range of 15°C to 55°C in comparison with ΔpH ≤ 1.346 for uncompensated output signal. This work establishes the framework for integration of ML techniques for drift compensation of ISFET chemical sensor to improve its performance.