LiNbO3 dynamic memristors for reservoir computing.

Information in conventional digital computing platforms is encoded in the steady states of transistors and processed in a quasi-static way. Memristors are a class of emerging devices that naturally embody dynamics through their internal electrophyiscal processes, enabling nonconventional computing paradigms with enhanced capability and energy efficiency, such as reservoir computing. Here, we report on a dynamic memristor based on LiNbO3. The device has nonlinear I-V characteristics and exhibits short-term memory, suitable for application in reservoir computing. By time multiplexing, a single device can serve as a reservoir with rich dynamics which used to require a large number of interconnected nodes. The collective states of five memristors after the application of trains of pulses to the respective memristors are unique for each combination of pulse patterns, which is suitable for sequence data classification, as demonstrated in a 5 × 4 digit image recognition task. This work broadens the spectrum of memristive materials for neuromorphic computing.

Intelligent Microsystem for Sound Event Recognition in Edge Computing Using End-to-End Mesh Networking.

Wireless acoustic sensor networks (WASNs) and intelligent microsystems are crucial components of the Internet of Things (IoT) ecosystem. In various IoT applications, small, lightweight, and low-power microsystems are essential to enable autonomous edge computing and networked cooperative work. This study presents an innovative intelligent microsystem with wireless networking capabilities, sound sensing, and sound event recognition. The microsystem is designed with optimized sensing, energy supply, processing, and transceiver modules to achieve small size and low power consumption. Additionally, a low-computational sound event recognition algorithm based on a Convolutional Neural Network has been designed and integrated into the microsystem. Multiple microsystems are connected using low-power Bluetooth Mesh wireless networking technology to form a meshed WASN, which is easily accessible, flexible to expand, and straightforward to manage with smartphones. The microsystem is 7.36 cm3 in size and weighs 8 g without housing. The microsystem can accurately recognize sound events in both trained and untrained data tests, achieving an average accuracy of over 92.50% for alarm sounds above 70 dB and water flow sounds above 55 dB. The microsystems can communicate wirelessly with a direct range of 5 m. It can be applied in the field of home IoT and border security.

Two-Dimensional Transition Metal Dichalcogenide Tunnel Field-Effect Transistors for Biosensing Applications.

Field-effect transistor (FET) biosensors based on two-dimensional (2D) materials have drawn significant attention due to their outstanding sensitivity. However, the Boltzmann distribution of electrons imposes a physical limit on the subthreshold swing (SS), and a 2D-material biosensor with sub-60 mV/dec SS has not been realized, which hinders further increase of the sensitivity of 2D-material FET biosensors. Here, we report tunnel FETs (TFETs) based on a SnSe2/WSe2 heterostructure and observe the tunneling effect of a 2D material in aqueous solution for the first time with an ultralow SS of 29 mV/dec. A bilayer dielectric (Al2O3/HfO2) and graphene contacts, which significantly reduce the leakage current in solution and contact resistance, respectively, are crucial to the realization of the tunneling effect in solution. Then, we propose a novel biosensing method by using tunneling current as the sensing signal. The TFETs show an extremely high pH sensitivity of 895/pH due to ultralow SS, surpassing the sensitivity of FET biosensors based on a single 2D material (WSe2) by 8-fold. Specific detection of glucose is realized, and the biosensors show a superb sensitivity (3158 A/A for 5 mM), wide sensing range (from 10-9 to 10-3 M), low detection limit (10-9 M), and rapid response rate (11 s). The sensors also exhibit the ability of monitoring glucose in complex biofluid (sweat). This work provides a platform for ultrasensitive biosensing. The discovery of the tunneling effect of 2D materials in aqueous solution may stimulate further fundamental research and potential applications.

Investigation of Self-Powered IoT Sensor Nodes for Harvesting Hybrid Indoor Ambient Light and Heat Energy.

Sensor nodes are critical components of the Internet of Things (IoT). Traditional IoT sensor nodes are typically powered by disposable batteries, making it difficult to meet the requirements for long lifetime, miniaturization, and zero maintenance. Hybrid energy systems that integrate energy harvesting, storage, and management are expected to provide a new power source for IoT sensor nodes. This research describes an integrated cube-shaped photovoltaic (PV) and thermal hybrid energy-harvesting system that can be utilized to power IoT sensor nodes with active RFID tags. The indoor light energy was harvested using 5-sided PV cells, which could generate 3 times more energy than most current studies using single-sided PV cells. In addition, two vertically stacked thermoelectrical generators (TEG) with a heat sink were utilized to harvest thermal energy. Compared to one TEG, the harvested power was improved by more than 219.48%. In addition, an energy management module with a semi-active configuration was designed to manage the energy stored by the Li-ion battery and supercapacitor (SC). Finally, the system was integrated into a 44 mm × 44 mm × 40 mm cube. The experimental results showed that the system was able to generate a power output of 192.48 µW using indoor ambient light and the heat from a computer adapter. Furthermore, the system was capable of providing stable and continuous power for an IoT sensor node used for monitoring indoor temperature over a prolonged period.