This paper presents a low-noise inverter-based current-mode instrumentation amplifier with tunable gain and bandwidth for electromyogram (EMG) and electrooculogram (EOG) biopotential signals, targeting low input noise while maintaining low power consumption. The gain tuning method is based on pseudo-resistors, whereas the bandwidth is tunable due to a varactor system that is controlled by the same control voltage that tunes the gain. The circuit was designed and manufactured using the 110 nm UMC CMOS technology node, occupying an area of 0.624 mm². The circuit presents a functioning mode for each biopotential signal with different characteristics, for the EMG a gain of 34.7 dB and a bandwidth of 1412 Hz was measured, with an input referred noise of 1.407 μV which matches a noise efficiency factor of 1.44. The EOG mode achieves a 39.5 dB gain and a 22.4 Hz bandwidth while presenting an input-referred noise of 0.829 μV corresponding to a noise efficiency factor of 6.37. For both modes, the supply voltage is 1.2 V and the circuit consumes 1 μA.

This work presents a modular acquisition platform for biasing and reading 2D crossbar resistive sensor arrays in AC mode based on the Zero Potential Method (ZPM). ZPM minimizes the number of connections and array bond pads while being scalable for arbitrary array dimensions. The system employs a new technique to suppress the non-informative sensor bias component thus allowing the readout of sensor arrays with a wide range of nominal resistances, validated from 1 kΩ to 1.8 MΩ. It can read up to 4 sensors simultaneously from a matrix comprising a maximum of 256 sensors. The platform is functionally demonstrated and characterized using a 4 × 4 resistive array and a 12 × 12 matrix of thin film spin valves, paving the way for a lab-on-chip Micro Total Analysis Systems (μTAS) device. The proposed system can also be employed in a range of applications like memristive reading and writing for neuromorphic systems, robotics sensing, and other applications based on crossbar resistive sensor matrices.

Touch is a crucial sense for advanced organisms, particularly humans, as it provides essential information about the shape, size, and texture of contacting objects. In robotics and automation, the integration of tactile sensors has become increasingly relevant, enabling devices to properly interact with their environment. This study aimed to develop a biomimetic, skin-inspired tactile sensor device capable of sensing applied force, characterizing it in three dimensions, and determining the point of application. The device was designed as a 4 × 4 matrix of tunneling magnetoresistive sensors, which provide a higher sensitivity in comparison to the ones based on the Hall effect, the current standard in tactile sensors. These detect magnetic field changes along a single axis, wire-bonded to a PCB and encapsulated in epoxy. This sensing array detects the magnetic field from an overlayed magnetorheological elastomer composed of Ecoflex and 5 µm neodymium–iron–boron ferromagnetic particles. Structural integrity tests showed that the device could withstand forces above 100 N, with an epoxy coverage of 0.12 mL per sensor chip. A 3D movement stage equipped with an indenting tip and force sensor was used to collect device data, which was then used to train neural network models to predict the contact location and 3D magnitude of the applied force. The magnitude-sensing model was trained on 31,260 data points, being able to accurately characterize force with a mean absolute error ranging between 0.07 and 0.17 N. The spatial sensitivity model was trained on 171,008 points and achieved a mean absolute error of 0.26 mm when predicting the location of applied force within a sensitive area of 25.5 mm × 25.5 mm using sensors spaced 4.5 mm apart. For points outside the testing range, the mean absolute error was 0.63 mm.