Noise Power Minimization in CMOS Brain-Chip Interfaces.

This paper presents specific noise minimization strategies to be adopted in silicon-cell interfaces. For this objective, a complete and general model for the analog processing of the signal coming from cell-silicon junctions is presented. This model will then be described at the level of the single stages and of the fundamental parameters that characterize them (bandwidth, gain and noise). Thanks to a few design equations, it will therefore be possible to simulate the behavior of a time-division multiplexed acquisition channel, including the most relevant parameters for signal processing, such as amplification (or power of the analog signal) and noise. This model has the undoubted advantage of being particularly simple to simulate and implement, while maintaining high accuracy in estimating the signal quality (i.e., the signal-to-noise ratio, SNR). Thanks to the simulation results of the model, it will be possible to set an optimal operating point for the front-end to minimize the artifacts introduced by the time-division multiplexing (TDM) scheme and to maximize the SNR at the a-to-d converter input. The proposed results provide an SNR of 12 dB at 10 µVRMS of noise power and 50 µVRMS of signal power (both evaluated at input of the analog front-end, AFE). This is particularly relevant for cell-silicon junctions because it demonstrates that it is possible to detect weak extracellular events (of the order of few µVRMS) without necessarily increasing the total amplification of the front-end (and, therefore, as a first approximation, the dissipated electrical power), while adopting a specific gain distribution through the acquisition chain.

A 28 nm Bulk-CMOS Analog Front-End for High-Rate ATLAS Muon Drift-Tube Detectors.

This paper presents the design and experimental characterization of a 28 nm Complementary Metal Oxide Semiconductor (CMOS) Analog Front-End (AFE) for fast-tracking small-diameter Muon Drift-Tube (sMDT) detectors. The device exploits an innovative analog signal processing that allows a strong increase in the detection rate of events and significantly reduces the impact of fake/pile-up events, which often corrupt incident radiation energy events. The proposed device converts the input charge coming from incident radiations into voltage by a dedicated Charge-Sensitive Preamplifier (CSPreamp). Therefore, the fast-tracking concept relies on sampling the slope of the CSPreamp output voltage and using it for detecting both the incident event arrival instant and the amount of charge that has been effectively read out by MDT detectors. This avoids the long processing times intrinsically needed for baseline recovery transient, during which the detected signal can be severely corrupted by additional and unwanted extra-events, resulting in extra-charge (and thus in CSP output voltage extra-transient) during the signal roll-off. The proposed analog channel operates with a 5-100 fC input charge range and has a maximum dead-time of 200 ns (against the 545 ns of the state-of-the-art). It occupies 0.03 mm2 and consumes 1.9 mW from 1 V of supply voltage.

The Evolution of Integrated Interfaces for MEMS Microphones.

Over the last decade, MEMS microphones have become the leading solution for implementing the audio module in most portable devices. One of the main drivers for the success of the MEMS microphone has been the continuous improvement of the corresponding integrated interface circuit performance in terms of both dynamic range and power consumption, which enabled the introduction in mobile devices of additional functionalities, such as Hi-Fi audio recording or voice commands. As a result, MEMS microphone interface circuits evolved from just simple amplification stages to complex mixed-signal circuits, including A/D converters, with ever improving performance. This paper provides an overview of such evolution based on actual design examples, focusing, finally, on the latest cutting-edge solutions.

Acoustic Analog Front End for Proton Range Detection in Hadron Therapy.

Clinical ions beams for cancer treatment provide maximum energy deposition (Bragg peak (BP)) at the end of their range and practically no dose behind. This enables a more efficient therapeutic option comparing with classical photon-based radiotherapy where maximum energy is deposited at the body interface. Obviously, minimum-error BP detection is, thus, a key aspect of this treatment. This paper investigates a promising detection technique, based on the so-called ionoacoustic effect. The BP energy deposition causes a small (millikelvin) heating of the surrounding volume that in turn induces a pressure variation. This generates a sound signal that can be detected by an acoustic sensor placed at a certain distance from the BP point. Thus, the sound time-of-flight measure aims, with sound speed, to detect the BP position with very high accuracy (<1 mm). This paper presents the results of a complete cross-domain model that starting from proton beam energy provides the induced pressure variation in water, emulates the propagation of sound waves in the medium, and finally, returns a voltage signal whose time evolution determines BP position with an average deviation from effective position of 1% accuracy.