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1. 128-Channel Multi-Chip Acoustic Hologram Generator

   https://doi.org/10.23919/VLSITechnologyandCir65189.2025.11075058  

   Kustin, John; Kang, Taewook; Song, Seungheun; Naveed, Yahya; Flynn, Michael P (June 2025, IEEE)

   An ASIC combines a numerically controlled oscillator with reconfigurable amplitude and phase control of 16 channels to synthesize acoustic holograms with a phased array of ultrasonic transmitters. Multiple chiplets operate synchronously to drive arbitrarily many channels. A scalable digital delay line technique, leveraging on-chip SRAM and single-bit noise-shaped bitstreams, realizes area-efficient, high-density, and high-resolution true-time-delay phase shifts. The prototype system employs 8 chiplets to drive a 128-element array for hologram generation. 

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2. Breaking AES-128: Machine Learning-Based SCA Under Different Scenarios and Devices

   https://doi.org/10.1109/CSR57506.2023.10225009  

   Tehranipoor, Sara; Karimian, Nima; Edmonds, Jack (July 2023, 2023 IEEE International Conference on Cyber Security and Resilience (CSR))

   Machine learning-based side-channel attacks (MLSCAs) have demonstrated the capability to extract secret keys from AES by learning the correlation between leakages from power traces or timing of AES execution. Previous work has focused on unmasked AES, the captured power traces for profiling and testing have been collected from the same device, and they are primarily implemented on microcontrollers. In this paper, we present a comprehensive MLSCA that considers both masked and unmasked AES running on software and hardware with a side-channel leakage model under four scenarios involving two target boards (Artix-7 XC7AI00T FPGAs and STM32F415 microcontrollers) and different keys for training and testing the model. Our implementation results indicate that support vector machines outperformed other machine learning techniques on masked software and unmasked software AES with only 4 traces. Long short-term memory networks were found to outperform other techniques on unmasked hardware AES (FPGA) with only 283 power traces. 

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3. A 128-ch Area-Efficient Neurochemical-Sensing Front-End for FSCV Recordings of Dopamine

   https://doi.org/10.1109/JSEN.2024.3359892  

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4. Effects of Droplet Volumes on Acoustothermal Heating in 128° YX LiNbO ₃ Substrates

   https://doi.org/10.1109/SENSORS52175.2022.9967169  

   Das, Pradipta Kr.; Huang, Yuqi; Evans-Nguyen, Theresa; Bhethanabotla, Venkat R. (October 2022, 2022 IEEE Sensors)

   Surface acoustic wave (SAW) devices can generate significant heat due to acoustic damping when liquid droplets are placed on them, and this heating (acoustothermal heating) can be used for microscale heating purposes. However, SAW devices are often used in biosensing applications where significant acoustothermal temperature rise can damage the proteins or the biomolecules and destroy the sensor performances. In this paper, we have performed thermal camera-based experiments to study the heating phenomena and how they can be controlled by varying droplet sizes. We found that the temperature rise linearly increases with increasing SAW power whereas it decreases with increasing droplet volume. Hence, a larger liquid volume and lower SAW power can be used in biosensors to avoid significant heating. 

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5. The Open Cluster Chemical Abundances and Mapping Survey. IV. Abundances for 128 Open Clusters Using SDSS/APOGEE DR16

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