This brief presents a 4 ^th -order continuous-time analog filter based on Flipped-Source-Follower stage. Source-Follower (SF) filters typically adopt pseudo-differential topology (critical for matching and bulk/substrate noise rejection) and are realized in not recent CMOS processes (130nm or 180nm) due to the intrinsic voltage headroom required by SF operation. The proposed device solves the above limitations by proposing a fully-differential circuital topology, which improves by 13 dB the power supply rejection with respect to pseudo differential approach, and by operating in 28nm-CMOS process, thanks to a proper level-shifter transistor, which enables optimum biasing point and enhances filter dynamic range. The prototype is composed by the cascade of two biquadratic cells. It features 90 MHz -3 dB bandwidth, and consumes 816 μW power (408 μW per cell) from a 1V supply. Dynamic-Range is 64 dB with 140 μV _RMS output noise, and at 0.32 V _0-PEAK differential output voltage swing. Figure-of-Merit is 156 dBJ $^{-1}$ .

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.

This paper presents the design and experimental characterization of a Proton Sound Detector (ProSD), a device that physically captures and senses the weak acoustic signal emitted by the fast energy deposition at the end of the same proton beam range. The measured acoustic signal Time-of-Flight provides a very accurate (13 μm accuracy) measure of the proton beam penetration depth in water, improving the proton range verification accuracy w.r.t. previous works in pre-clinical scenarios. This suggests interesting possibilities for high-accuracy and real-time beam monitoring and calibration in hadron-therapy for cancer treatment. The detector has been fully characterized and tested with a physical 20 MeV proton beam in a water energy absorber. The ProSD and the water tank have been mounted in front of the exit layer of a 20 MeV 120 ns pulse time-width proton beam. A clear sinusoidal-like acoustic signal of 5 Pa and 2.3 MHz frequency has been detected at 12 dB SNR with 0.8 Gy single shot dose. After averaging 10 beam shots the achieved Signal-to-Noise-Ratio is 22 dB allowing a ±7.5 μm precision vs. previously reported ±45μm ionoacoustic precision.

Proton sound detectors rely on sensing the weak thermoacoustic signals emitted by the fast energy deposition at the end of the beam penetration path through the energy absorber. The energy of the ions/protons is first converted into heat, and then, into pressure by a thermodynamic process. The pressure signal propagates through the energy absorber, and it is read by an acoustic sensor, which, in turn, converts the pressure wave into an analog electrical voltage. Such an analog signal is digitalized by the analog front-end. Its digital representation is used for the measurement of the beam depth. This emerging technique attracts attention in both physics experiments and medical applications (hadron therapy). This article investigates the signal-to-noise-ratio performance in proton sound detectors exploring all critical steps that lead to both reduction of the power of the electrical signal and noise power increasing. Nonetheless, this article proposes specific technical solutions to mitigate such signal-to-noise-ratio degradations with particular attention to higher energy (i.e., 200 MeV) proton beams, which are crucial for medical applications, and where the maximum achievable signal-to-noise ratio can be 50/60 dB lower than 20 MeV acoustic pulses.

Real-time neural spike detection is an important step in understanding neurological activities and developing brain-silicon interfaces. Recent approaches exploit minimally invasive sensing techniques based on implanted complementary metal-oxide semiconductors (CMOS) multi transistors arrays (MTAs) that limit the damage of the neural tissue and provide high spatial resolution. Unfortunately, MTAs result in low signal-to-noise ratios due to the weak capacitive coupling between the nearby neurons and the sensor and the high noise power coming from the analog front-end. In this paper we investigate the performance achievable by using spike detection algorithms for MTAs, based on some variants of the smoothed non-linear energy operator (SNEO). We show that detection performance benefits from the correlation of the signals detected by the MTA pixels, but degrades when a high firing rate of neurons occurs. We present and compare different approaches and noise estimation techniques for the SNEO, aimed at increasing the detection accuracy at low SNR and making it less dependent on neurons firing rates. The algorithms are tested by using synthetic neural signals obtained with a modified version of NEUROCUBE generator. The proposed approaches outperform the SNEO, showing a more than 20% increase on averaged sensitivity at 0 dB and reduced dependence on the neuronal firing rate.

This paper presents a multidisciplinary experiment where a population of neurons, dissociated from rat hippocampi, has been cultivated over a CMOS-based micro-electrode array (MEA) and its electrical activity has been detected and mapped by an advanced spike-sorting algorithm implemented on FPGA. MEAs are characterized by low signal-to-noise ratios caused by both the contactless sensing of weak extracellular voltages and the high noise power coming from cells and analog electronics signal processing. This low SNR forces to utilize advanced noise rejection algorithms to separate relevant neural activity from noise, which are usually implemented via software/off-line. However, off-line detection of neural spikes cannot be obviously used for real-time electrical stimulation. In this scenario, this paper presents a proper FPGA-based system capable to detect in real-time neural spikes from background noise. The output signals of the proposed system provide real-time spatial and temporal information about the culture electrical activity and the noise power distribution with a minimum latency of 165 ns. The output bit-stream can be further utilized to detect synchronous activity within the neural network.