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Nanoelectromechanical systems, or NEMS, are MEMS scaled to submicron dimensions. In this size regime, it is possible to attain extremely high fundamental frequencies while simultaneously preserving very high mechanical responsivity (small…
MIMOSIS is a CMOS Monolithic Active Pixel Sensor developed to equip the Micro Vertex Detector of the Compressed Baryonic Matter (CBM) experiment at FAIR/GSI. The sensor will combine an excellent spatial precision of $5~\mu m$ with a time…
Magnetic random access memory (MRAM) is a leading emergent memory technology that is poised to replace current non-volatile memory technologies such as eFlash. However, the scaling of MRAM technologies is heavily affected by…
The deployment of machine learning (ML) models on microcontrollers (MCUs) is constrained by strict energy, latency, and memory requirements, particularly in battery-operated and real-time edge devices. While software-level optimizations…
In recent years, a considerable research effort has shown the energy benefits of implementing neural networks with memristors or other emerging memory technologies. However, for extreme-edge applications with high uncertainty, access to…
We present a novel approach to EEG decoding for non-invasive brain machine interfaces (BMIs), with a focus on motor-behavior classification. While conventional convolutional architectures such as EEGNet and DeepConvNet are effective in…
Online monitoring remains an important requirement for a range of microsystems. The solution based on the injection of an actuating test stimulus into the bias structure of active devices holds great potential. This paper presents an…
We present the design and implementation of a scanning probe microscope, which combines electrically detected magnetic resonance (EDMR) and (photo-)conductive atomic force microscopy ((p)cAFM). The integration of a 3-loop 2-gap X-band…
Neuromorphic architectures, which incorporate parallel and in-memory processing, are crucial for accelerating artificial neural network (ANN) computations. This work presents a novel memristor-based multi-layer neural network (memristive…
This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems. Due to its computational efficiency and the fact that training…
We introduce a novel electro-optomechanic neural sensor for realizing ultra-compact neural recording probes that can detect and relay electrophysiology signals from within neural tissue. This technology addresses outstanding challenges…
Efficient design and performance of electrically actuated MEMS devices necessitate accurate estimation of electrostatic forces on the MEMS structures. This in turn requires thorough study of the capacitance of the structures and finally the…
Due to the rapid growth in demand for power for sensing devices located in remote locations, scientists' attention has been drawn to vibration energy harvesting as an alternative to batteries. As a result of over two decades of micro-scale…
Compute-in-memory (CIM) accelerators for spiking neural networks (SNNs) are promising solutions to enable $\mu$s-level inference latency and ultra-low energy in edge vision applications. Yet, their current lack of flexibility at both the…
The reservoir computing paradigm is employed to classify heartbeat anomalies online based on electrocardiogram signals. Inspired by the principles of information processing in the brain, reservoir computing provides a framework to design,…
Recent scientific and technological advances have enabled the detection of gravitational waves, autonomous driving, and the proposal of a communications network on the Moon (Lunar Internet or LunaNet). These efforts are based on the…
Objective. Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, dense high-frequency processing is often…
This paper proposes a wearable-controlled mobile manipulator system for intelligent smart home assistance, integrating MEMS capacitive microphones, IMU sensors, vibration motors, and pressure feedback to enhance human-robot interaction. The…
We present a theoretical proposal and simulation study of a digital closed-loop thermal atomic-beam interferometer for inertial navigation applications. The scheme synchronizes phase biasing with momentum-kick reversal through the atomic…
Low-cost micro-electromechanical accelerometers are widely used in navigation, robotics, and consumer devices for motion sensing and position estimation. However, their performance is often degraded by bias errors. To eliminate…