Energy Autonomous Wearable Sensors (EAWS) have attracted a large interest due to their potential to provide reliable measurements and continuous bioelectric signals, which help to reduce health risk factors early on, ongoing assessment for disease prevention, and maintaining optimum, lifelong health quality. This review paper presents recent developments and state-of-the-art research related to three critical elements that enable an EAWS. The first element is wearable sensors, which monitor human body physiological signals and activities. Emphasis is given on explaining different types of transduction mechanisms presented, and emerging materials and fabrication techniques. The second element is the flexible and wearable energy storage device to drive low-power electronics and the software needed for automatic detection of unstable physiological parameters. The third is the flexible and stretchable energy harvesting module to recharge batteries for continuous operation of wearable sensors. We conclude by discussing some of the technical challenges in realizing energy-autonomous wearable sensing technologies and possible solutions for overcoming them.

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In this paper, we showcase the innovative concept of implementing Oscillatory Neural Networks (ONNs) for neuromorphic computing with beyond-CMOS devices based on vanadium dioxide to mimic neurons and resistors to emulate synapses. We explore ONN technology potentials from device to analog circuit-level simulations. We report that ONN behaves like an associative memory and can implement energy-based models such as Hopfield Neural Networks on edge devices. Finally, as a proof of concept, a reconfigurable digital ONN is implemented on FPGA for pattern recognition tasks.

variety of flexible electronic devices such as tattoo-like sensory skin patches, [1-3] energy harvesters, [4] energy storage elements, [5,6] stretchable interconnects. [7] The astonishing progress in the technologies mentioned above have enabled a new technological drive called "Internet-of-Medical-Things (IoMT)," linking wearable devices/ sensors into a communication network for real-time or periodic patient-doctor communications. [8] The IoMT will help people to better monitor their health status by collecting subtle vital signs modifications and transferring to healthcare service providers. Part of the ongoing research efforts to develop IoMT is focused on the development of wearable sensors that can be embedded in clothing, [9-11] wrist watches, patches [12] and bands, [13-16] contact lenses, [17-21] tattoo-like sensory skin patches (electronic skin), [2,22] etc. to enable around-the-clock vital signs monitoring, human-machine interactive systems, [2] and as bionic ligaments in soft robotics. [23] Among various types of wearable mechanical sensors such as piezoresistive, piezoelectric, and capacitive, the piezoresistive-based strain sensors are gaining a lot of interest as they provide high sensitivity with simple device design and readout circuits [24-30] for vital signs monitoring and soft robotics. Piezoresistive strain sensors are based on the change of electrical properties of the material when subjected to mechanical deformation. Piezoresistive This paper describes a facile strategy of micromolding-in-capillary process to fabricate stretchable strain sensors wherein, the sensing material is wrapped within silicone rubber (Dragon Skin [DS]) to form a sandwichlike structure. Two different 1D sensing materials are exploited to fabricate and study strain sensing performance of such device structure, namely multi-walled carbon nanotubes (MWCNTs) and silver nanowires (AgNWs). The fabricated strain sensors using MWCNT exhibits wide sensing range (2-180%), and moderately high sensing performance with outstanding durability (over 6000 cycles). It is found that MWCNT presents a strong straindependent character in the 45-120% elongation regime. On the other hand, very high gauge factor of >10 6 is achieved using AgNWs at 30% strain with good stability (over 100 cycles). Sensing mechanisms for both 1D conductive sensing materials are discussed. They can be applied for human motion monitoring such as finger, knee, and wrist bending movements to enable human physiological parameters to be registered and analyzed continuously. They are also employed in multichannel and interactive electronic system to be used as a control mechanism for teleoperation for robotic end-effectors. The developed sensors have potential applications in health diagnosis and human-machine interaction.

In a standard industrial approach the timing performance verification is obtained using a tabular method that necessitates a great amount of simulations. They must specify, for each drive in each logic family: the load, the input slope, the temperature and the supply voltage sensitivity, for each edge, of the transition time and propagation delay. Extending a logical effort like based model of timing performance of CMOS structures we show in this paper that it is possible to define-a specific performance representation allowing a continuous representation of the performance sensitivity of a complete family. We describe the parameter calibration procedure and validate the proposed representation on a 0.13pm process by comparing the performance sensitivity deduced from this representation to sensitivity values obtained from electrical simulations performed with the full process model of the foundry.