As semiconductor devices become miniaturized and their structures become more complex, there is a growing need for large-scale atomic-level simulations as a less costly alternative to the trial-and-error approach during development.Although machine learning force fields (MLFFs) can meet the accuracy and scale requirements for such simulations, there are no open-access benchmarks for semiconductor materials.Hence, this study presents a comprehensive benchmark suite that consists of two semiconductor material datasets and ten MLFF models with six evaluation metrics. We select two important semiconductor thin-film materials silicon nitride and hafnium oxide, and generate their datasets using computationally expensive density functional theory simulations under various scenarios at a cost of 2.6k GPU days.Additionally, we provide a variety of architectures as baselines: descriptor-based fully connected neural networks and graph neural networks with rotational invariant or equivariant features.We assess not only the accuracy of energy and force predictions but also five additional simulation indicators to determine the practical applicability of MLFF models in molecular dynamics simulations.To facilitate further research, our benchmark suite is available at https://github.com/SAITPublic/MLFF-Framework.

Electric vehicles require monitoring of car batteries. Wireless sensing is desirable to eliminate connecting wires, resulting in low installation and maintenance cost. This paper presents a low-power wireless sensor node and an interface circuit to monitor the battery pack of an electric vehicle. To save power, the proposed sensor node adopts the wake-up/sleep mode and reduces the transmission time through compaction of the data to transmit. The wireless sensor node is prototyped with a Texas Instruments low power microcontroller CC1310, where CC1310 embeds a variant of Zigbee radio and a 12-bit analog-to-digital converter. The prototype monitors the voltage and temperature of nine battery cells and the battery pack current. The measured power dissipation of the prototype is, on average, 1.2 mW. Considering the power dissipation of the microcontroller is 22 mw during transmission, the proposed method reduces the power substantially.

Real time locating systems (RTLS) determine and track the location of assets and person using active tags. Two or more readers can estimate the tag's range from each reader and determine its location. In order to determine tag's location, there are several methods. This paper presents a real time locating system which is based on time difference of arrival(TDOA) of a signal, for wireless networks using IEEE 802.15.4 radio. In order to measure the time that a signal arrives at, exact time measurement is crucial. This paper proposes a multi-phase radio method to provide exact time measurement. In addition, to calculate the time difference, readers in the network should be synchronized with themselves. We also present a precision time synchronization protocol. This paper includes the performance evaluation. The performance shows that our RTLS has accuracy of within 3 meters.