Archivio della ricerca - Fondazione Bruno Kessler
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Analysis of a low frequency MEMS capacitive accelerometer under the effect of biasing voltage for detection of Parkinsons tremor
No full textThe paper presents a comprehensive analysis of the design and simulation of a MEMS differential capacitive accelerometer optimized for the detection of tremor signals in Parkinson’s disease patients. The accelerometer design aims to address the sensing challenges at very low frequencies (< 10 Hz) associated with Parkinson’s tremors, specifically targeting the frequency range of 3.5–7.5 Hz. The design process considers various parameters to optimize the resonant frequency, mechanical stability, and sensitivity of the accelerometer. Finite element analysis (FEA) using COMSOL Multiphysics validates the design approach, demonstrating a resonant frequency of 3.5 Hz with a maximum displacement of 1.77 μm at an acceleration of 0.04 g at a biasing voltage of 10 V. This proposed design exhibits a noteworthy mechanical sensitivity of 44.25 μm and an electrical sensitivity of 1.428 nF/g, emphasizing its capacity to detect and respond to minute physical and electrical changes with high precision. Analytical models are developed to calculate the resonant frequency and effective spring constant, which further characterizes the accelerometer’s mechanical behavior. The proposed design achieves a comparable dynamic range, high sensitivity, linear response, and minimal cross sensitivity when compared with existing literature. The proposed MEMS differential capacitive accelerometer exhibits significant potential for precise measurement and quantification of tremor signals in individuals afflicted with Parkinson’s disease. By accurately capturing and analyzing these tremor signals, this accelerometer has the capacity to contribute significantly to the advancement of medical diagnosis and monitoring in the field of Parkinson’s disease
Copolymer vs interpenetrated polymer network microgels: The case of poly(N-isopropylacrylamide) and poly(acrylic acid)
No full textCPAD: Constraint-Projected Adversarial Attack for Dependable Network Intrusion Detection
No full textMachine-learning Network Intrusion Detection Systems (NIDSs) are increasingly used to detect network attacks, but they remain vulnerable to Adversarial Machine Learning (AML) attacks that subtly perturb traffic to cause misclassification. Understanding and testing these attacks is essential for deploying reliable NIDS in real networks. However, existing AML attack generators, often developed for other application domains, frequently violate network constraints or alter the malicious functionality of traffic, limiting their usefulness for realistic evaluation. To address these limitations, we propose Constraint-Projected Adversarial Attack (CPAD), a method that preserves both validity and maliciousness while inducing evasion. The main goal of CPAD is to allow cybersecurity analysts and practitioners to assess the robustness of their NIDSs against adversarially perturbed network attacks. CPAD partitions features into perturbable and non-perturbable sets, iteratively targets the most influential features, and projects perturbations back into the allowable distribution and constraint space so samples remain realistic and functional. Evaluated on Brute Force, DDoS, DoS, and Bot attacks using a deep model, CPAD produces realistic adversarial samples that retain attack characteristics and successfully evade detection, enabling more faithful robustness assessment of NIDSs
Quasi-3D Nanoscale Fabrication: Precision Patterning and Modelling via Focused Ion Beam Implantation
No full textIn this work, we present recent advances in the development and modelling of quasi-3D silicon nanostructures defined by focused ion beam (FIB) technologies together with tetramethylammonium hydroxide (TMAH) etching. The development of FIB columns equipped with liquid metal alloy ion sources (LMAIS) opens new opportunities for FIB-based processing, enabling multi-species patterning [1]. Here, we report the use of focused Au+ and Ga+ ion implantation to create hard masks for nanolithography on silicon, focusing on the modelling of the implantation and etching processes to control the thickness and height/depth to the ultimate sub-nm range. Our aim is to develop a predictive model for the etching behavior of FIB-implanted volumes, enabling the design of corrugated and 3D suspended silicon structures with tailored dimensions. Previous studies have demonstrated that Ga+ ion implantation in silicon via FIB serves as an efficient and straightforward resistless lithography technique [2-5]. This approach facilitates the fabrication of nanometer-scale structures on silicon and other materials. The implanted Ga+ volume enhances silicon's resistance to both wet and dry etching, enabling its use as an etching hard mask or as a functional device (Figure 1). The resolution of the structures is largely determined by the focused beam's diameter and the ion penetration depth and straggling. Several applications of this method have been reported, including the fabrication of suspended silicon nanowires [6] and single-electron devices [7].
We have modelled the process to precisely control the vertical dimensions (z-direction) of silicon nanostructures, including both suspended and non-suspended configurations. Process-calibration experiments were conducted for Ga+ using a 30 keV Crossbeam 550L system (Zeiss) and for Au+ using a 35 keV Velion FIB-SEM system (Raith). Fluence values ranged from 1·1014 at/cm2 to 1·1017 at/cm2, avoiding lower doses (insufficient) and higher dose rates (entering milling regimes) [5]. The implanted samples were etched using TMAH at 25% concentration and 80°C. Atomic force microscopy (AFM) was used to characterize the structures post-implantation and post-etching. After calibration, we developed algorithms in Matlab software to predict the etching rates in TMAH and the final etching depth (Figure 2) for both Au+ and Ga+ ion species as a function of implantation dose. A custom Matlab framework was chosen to enable modeling flexibility, integration of experimental parameters and simulations tailored to the specific scope of implantation and etching dynamics. This information is crucial for fine-tuning the dimensions of corrugated and 3D suspended silicon structures. Figure 3 shows the silicon surface after Ga+ implantation and the corrugated silicon surface that results from the subsequent etching step.
In this study, we successfully demonstrated the development and modelling of silicon nanostructures using FIB implantation with both Ga and Au ion species. Through the detailed process-calibration experiments and the development of predictive algorithms, we achieved precise control over the etching behavior of ion-implanted silicon in TMAH, enabling the fabrication of highly defined, suspended 3D nanostructures (Figure 4). Our results show that the combination of FIB implantation and wet etching provides an effective route for creating complex 3D silicon nanostructures, opening new possibilities for fabricating nanoscale electronic and sensing devices.
References:
[1] L. Bischoff et al. Appl Phy Rev 3, 021101 (2016);
[2] J. Brugger et al. Microelect. Eng, 35, pp. 401-404 (1997);
[3] B. Schmidt et al. Sens. Actuators Phys, 61, 369-373 (1997);
[4] G. Rius et al. J. Vac. Sci. Technol, 27, 6, p. 2691 (2009);
[5] J. Llobet et al. Nanotechnology, 25, 13, p. 135302 (2014);
[6] J. Llobet et al. Appl. Phys. Lett., 107, 7, p. 073104 (2015);
[7] J. Llobet et al. Appl. Phys. Lett., 107, 22, p. 223501 (2015
Evaluating Linguistic Speaker Profiles on Response Selection in Multi-Party Dialogue
No full textWe investigate whether incorporating linguistically derived speaker profiles improves the response selection capabilities of instruction-tuned large language models (LLMs) in multi-party dialogues. Using the Wikipedia Talk Page dataset, we construct lightweight profiles for each speaker based on features extracted from their prior messages, including frequent nouns and verbs, and sentiment tendency. These profiles are incorporated into the input prompts and evaluated using in-context learning with LLaMA 3.2 Instruct (1B and 8B) and GPT-4o, without any model fine-tuning. We compare performance across models and prompt settings, with and without speaker profiles, and analyze the effect of different profile configurations. Results are compared against a Random baseline and a supervised Siamese RNNs (with GRU units) trained on the same data. Our results show that incorporating speaker profiles improves response selection performance across most LLM settings, with the strongest gains observed in larger models such as LLaMA 3.2 (8B). Lexical features (frequent nouns and verbs) demonstrate greater improvements than sentiment information, particularly in low-context or underspecified scenarios. However, profile effectiveness varies by model scale and prompt format, and provides limited benefit in cases where distractors are lexically and semantically similar to the ground-truth response
Quantitative determination of spatial resolution and linearity of position-sensitive LG-SiPMs at sub-millimeter scale via Ricean distribution fitting
No full textPosition-sensitive SiPMs are useful in all light detection applications requiring a small number of readout channels while preserving the information about the incoming light’s interaction position. Focusing on a
2 × 2 array of LG-SiPMs covering an area of ∼ 15.5 × 15.5 mm2 with just 6 readout channels, we proposed a quantitative method to evaluate the image reconstruction performance. The method is based on a statistical
approach to assess the device’s precision (spatial resolution) and accuracy (linearity) in reconstructing the light spot center of gravity. This evaluation is achieved through a Rice probability distribution function fitting. We
obtained an average sensor spatial resolution of 81±3 μm (standard deviation) with a corresponding accuracy of 231 ± 4 μm