The unprecedented growth in the complexity of state-of-the-art deep neural networks (DNNs) is inevitably accompanied by an exponential increase in the computational requirements of the core hardware. The reason for this enormous computational demand is that the input data within the DNN is processed by a chain of many matrix multiplications using digital processors. In fact, this represents a bottleneck in the implementation of DNNs in digital devices, which is additionally accompanied by the escalating power consumption of these systems. An elegant solution to these challenges is provided by a paradigm shift in DNN topology that favors implementation in analog computers. In this paper, we show that a ferroelectric field-effect transistor (FeFET) can serve as a fundamental component in analog computers for DNNs. We provide phase-field simulations of a system where the ferroelectric layer is deposited on the semiconductor substrate to form a fundamental basis of the FeFET. We consider a domain pattern in the ferroelectric layer that controls the voltage of the channel between the source and drain of the FeFET. We show that changing the geometric parameters of the domain pattern allows continuous control of the current between the source and drain in the FeFET. We discuss how these principles and features of the FeFET can be beneficially used for the construction of the analog processor and how such an analog processor can be used for the implementation of the DNN. Further, we discuss the concepts and topologies of FeFETs during DNN training and input data processing. We discuss the advantages of the designed system in the implementation of neural networks.

The ongoing increase in worldwide energy consumption and impressive progress in renewable energy sources have generated a pressing need for energy storage systems. Solid-state dielectric capacitors, which can rapidly store and discharge electrical charges, present benefits compared to batteries and electrochemical capacitors in high-power electronic applications. The requirement to minimize the size and cost of insulation technology in next generation electronic devices underscores the importance of the development of dielectric thin film with remarkable energy storage density and efficiency, particularly under conditions of low voltages and high temperatures. In the present work, we will discuss the roles of the imprint, defects and epitaxial strain to significantly enhance the energy storage density (URec) and efficiency (η), in particularly, at low electric fields/voltages in high-quality epitaxial relaxor ferroelectric Pb(Mg1/3Nb2/3)O3–xPbTiO3 (PMN–PT) thin films grown by pulsed laser deposition technique. For instance, by using a synergistic effect of large built-in electric field > 50 kV/cm, dipole defects and strain gradients, we successfully designed PMN–PT thin films with relaxor Antiferroelectric-like behavior, characterized by ultra-high polarization difference (ΔP=Pm-Pr), slim P-E loops which permitted to achieve high energy storage performances at low and moderate electric fields. For instance, at 1.4 MV/cm, the as grown PMN-33PT thin film exhibited ΔP ≈ 80μC/cm2, URec≈24 J/cm3, and η ≈ 88%.

Cough is the most common symptom prompting individuals to seek medical advice. However, the widespread adoption of autonomous cough monitoring using wearable devices remains limited. This talk introduces a wireless cough monitoring device utilizing piezoelectric energy harvesting technology. The design emphasizes cost-effectiveness and energy efficiency, allowing simple attachment onto human skin using medical-grade tapes. The device's standout feature lies in its departure from continuously recording real-time acoustic data at a high sampling rate, as commonly employed in prior works. Instead, it capitalizes on the energy harvesting capability, utilizing harvested energy from muscle movements induced by coughing as crucial information. The energy harvested within specific intervals translates into a historical record of cough occurrences during that timeframe. This Energy-as-Data protocol substantially reduces the device's duty cycle, resulting in a remarkable extension of battery life by up to 2100%. Notably, this extension is achieved while maintaining reasonable accuracy in cough monitoring. With this capability, the device can autonomously monitor and analyze cough data from both in- and outpatients, serving daily, research, and clinical purposes. Its potential extends to enhancing prediction and management of severe respiratory diseases.