Segmented high-purity germanium detectors are at the heart of cutting-edge gamma spectroscopy. However, their technical and manufacturing specifications are not known, since they are provided by industry. To bridge this gap, Italy’s INFN launched the N3G Project, an initiative to design new segmented high-purity germanium detectors while acquiring the technical expertise to build, manage, and maintain them independently. In this framework, the current paper offers a novel contribution. The innovative system designed to electrically connect the electrodes of the detector to the front-end electronics will be described and its low impact on the detector leakage current will be shown. Moreover, the architecture of a new integrated charge sensitive pre-amplifier will be reported and its validation will be presented. The circuit demonstrated excellent linearity and low noise. An integral non-linearity factor of $\mathbf{0. 4} \%$ was found and an optimum equivalent noise charge of 108 root-meansquare electrons was measured for a 15 pF equivalent electrode capacitance.
In this paper, a comparative analysis is performed between two techniques previously presented in the literature for generating voltage references ($V_{R E F}$), focusing on their stability with respect to variations in temperature and process parameters, in the context of integrated micropower management circuits. The first technique combines Proportional-to-Absolute-Temperature (PTAT) and Complementary-to-AbsoluteTemperature (CTAT) currents generated with the support of operational amplifiers, to generate the ($V_{R E F}$). The second technique utilizes a self-biased current mirror for generating a PTAT current, while the CTAT current is again generated with the previously used opamp-based circuit, and both currents are added to generate the $V_{R E F}$. A band-gap reference (BGR) design involves a bandgap core, a summing circuit, and a start-up circuit to apply the correct bias. Both BGR1 and BGR2 circuitry provides a voltage reference of 900 mV over $-40^{\circ} \mathrm{C}$ to $100^{\circ} \mathrm{C}$ temperature range with a power supply (VDD) of 1.2 V.
The growing demand for reliable wearable devices that can continuously monitor vital signs and track health under various conditions, impose challenging constraints on battery life. Wearable devices typically include a photoplethysmogram (PPG) sensor, which is used for various applications such as monitoring heart rate (HR) and blood oxygenation (SpO2). The efficiency of these applications depends on the quality of the PPG sensor, which acquires raw data through the analog front-end and transmits it externally. This paper presents a novel Register Transfer Level (RTL) block that evaluates the quality of the PPG signal. This Signal Quality Assessment (SQA) RTL block is derived from postprocessing algorithms and converted into a real-time singlesample evaluation algorithm and provides significant benefits to the sensor with minimal overhead in terms of energy and area consumption.
An analog integrated decision tree classifier designed for real-time dementia prediction with low power consumption is presented in this work. The design uses sub-threshold analog circuitry, including a squarer circuit, analog multiplier, and current comparator, to achieve an exceptionally low power consumption of $729 n W$ and a classification speed of $570 K$ inferences per second. It is developed using the TSMC 65nm CMOS process. The classifier consistently performed well despite process, voltage, and temperature fluctuations, achieving an average accuracy of $90.63 \%$ when tested on a dataset representative of machine operating conditions. Through the prediction of dementia neurological disorder, this method offers effective, real-time, edge-based classification in biomedical settings.
