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1. Machine Learning Augmentation in Micro-Sensor Assemblies

   https://doi.org/10.1115/DETC2020-22665  

   Hasan, Mohammad H.; Alsaleem, Fadi; Abbasalipour, Amin; Pourkamali Anaraki, Siavash; Emad-Un-Din, Muhammad; Jafari, Roozbeh (August 2020, 14th International Conference on Micro- and Nanosystems (MNS))

   null (Ed.)

   Abstract The size and power limitations in small electronic systems such as wearable devices limit their potential. Significant energy is lost utilizing current computational schemes in processes such as analog-to-digital conversion and wireless communication for cloud computing. Edge computing, where information is processed near the data sources, was shown to significantly enhance the performance of computational systems and reduce their power consumption. In this work, we push computation directly into the sensory node by presenting the use of an array of electrostatic Microelectromechanical systems (MEMS) sensors to perform colocalized sensing-and-computing. The MEMS network is operated around the pull-in regime to access the instability jump and the hysteresis available in this regime. Within this regime, the MEMS network is capable of emulating the response of the continuous-time recurrent neural network (CTRNN) computational scheme. The network is shown to be successful at classifying a quasi-static input acceleration waveform into square or triangle signals in the absence of digital processors. Our results show that the MEMS may be a viable solution for edge computing implementation without the need for digital electronics or micro-processors. Moreover, our results can be used as a basis for the development of new types of specialized MEMS sensors (ex: gesture recognition sensors). 

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2. Viscoelastic Influence On the Board Level Assessment of Wafer Level Packages Under Drop Impact and Under Thermal Cycling

   https://doi.org/10.1115/1.4054784  

   Misrak, Abel; Bhandari, Rabin; Agonafer, Dereje (June 2022, Journal of Electronic Packaging)

   Abstract Structural components such as printed circuit boards (PCBs) are critical in the thermomechanical reliability assessment of electronic packages. Previous studies have shown that geometric parameters such as thickness and mechanical properties like elastic modulus of PCBs have direct influence on the reliability of electronic packages. Elastic material properties of PCBs are commonly characterized using equipment such as tensile testers and used in computational studies. However, in certain applications viscoelastic material properties are important. Viscoelastic influence on materials is evident when one exceeds the glass transition temperature of materials. Operating conditions or manufacturing conditions such as lamination and soldering may expose components to temperatures that exceed the glass transition temperatures. Knowing the viscoelastic behavior of the different components of electronic packages is important in order to perform accurate reliability assessment and design components such as printed circuit boards (PCBs) that will remain dimensionally stable after the manufacturing process. Previous researchers have used creep and stress relaxation test data to obtain the Prony series terms that represent the viscoelastic behavior and perform analysis. Others have used dynamic mechanical analysis in order to obtain frequency domain master curves that were converted to time domain before obtaining the Prony series terms. In this paper, nonlinear solvers were used on frequency domain master curve results from dynamic mechanical analysis to obtain Prony series terms and perform finite element analysis on the impact of adding viscoelastic properties when performing reliability assessment. The computational study results were used to perform comparative assessment to understand the impact of including viscoelastic behavior in reliability analysis under thermal cycling and drop testing for Wafer Level Chip Scale Packages. 

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3. MEMS Actuators for Optical Microendoscopy

   https://doi.org/10.3390/mi10020085  

   Qiu, Zhen; Piyawattanametha, Wibool (February 2019, Micromachines)

   Growing demands for affordable, portable, and reliable optical microendoscopic imaging devices are attracting research institutes and industries to find new manufacturing methods. However, the integration of microscopic components into these subsystems is one of today’s challenges in manufacturing and packaging. Together with this kind of miniaturization more and more functional parts have to be accommodated in ever smaller spaces. Therefore, solving this challenge with the use of microelectromechanical systems (MEMS) fabrication technology has opened the promising opportunities in enabling a wide variety of novel optical microendoscopy to be miniaturized. MEMS fabrication technology enables abilities to apply batch fabrication methods with high-precision and to include a wide variety of optical functionalities to the optical components. As a result, MEMS technology has enabled greater accessibility to advance optical microendoscopy technology to provide high-resolution and high-performance imaging matching with traditional table-top microscopy. In this review the latest advancements of MEMS actuators for optical microendoscopy will be discussed in detail. 

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4. LCD 3D PRINTING OF A NORMALLY CLOSED FLUIDIC TRANSISTOR VIA ADDITIVE ASSEMBLY

   Chowdhury, A_Muhaymin; Stine, Thaddaeus; Upadhayay, Kalp B; Catan, Carolyn G; Kidambi, Adithya; Eclarin, Althea_Marielle G; Lim, Catherine W; Liu, Michelle; Sochol, Ryan D (June 2025, Transducers Research Foundation)

   Fluidically actuated soft robotic devices have attracted increasing interest due to their ability to provide benefits over traditional rigid systems in biomedical applications such as minimally invasive surgery, rehabilitative devices, and prosthetics. Unfortunately, challenges remain for controlling the fluidic operations of such systems, driving a critical need for new classes of fluidic circuit elements. Here we explore the use of “Liquid Crystal Display (LCD)” 3D printing—a low-cost vat photopolymerization (VPP) approach—to additively manufacture “normally closed” fluidic transistors with operations analogous to their electronic counterparts. Specifically, we leverage an “additive assembly” strategy wherein part components are printed separately and assembled post hoc. Experimental results for higher source pressure magnitudes revealed that in the absence of an applied gate pressure, the element obstructed source-to-drain fluid flow—i.e., normally closed behavior—however, by applying a gate pressure of ≥25 kPa, the element permitted source-to-drain fluid flow. Thus, this work establishes the efficacy for VPP-based additive assembly of fluidic circuit elements, which could help to advance and democratize fluidic circuit-based soft robotic technologies. 

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5. A Novel FPGA-Based Circuit Simulator for Accelerating Reinforcement Learning-Based Design of Power Converters

   https://doi.org/10.1109/ASAP57973.2023.00013  

   Xu, Zhenyu; Yu, Miaoxiang; Yang, Qing Yang; Jeong, Yeonho; Cai, Jillian; Wei, Tao (July 2023, 34th IEEE International Conference on Application-specific Systems, Architectures and Processors.)

   Abstract: High-efficiency energy conversion systems have become increasingly important due to their wide use in all electronic systems such as data centers, smart mobile devices, E-vehicles, medical instruments, and so forth. Complex and interdependent parameters make optimal designs of power converters challenging to get. Recent research has shown that machine learning (ML) algorithms, such as reinforcement learning (RL), show great promise in design of such converter circuits. A trained RL agent can search for optimal design parameters for power conversion circuit topologies under targeted application requirements. Training an RL agent requires numerous circuit simulations. It requires significantly more training iterations when the tolerance of circuit components due to manufacturing inconsistency, aging, and temperature variation is considered. As a result, they may take days to complete, primarily because of the slow time-domain circuit simulation. This paper proposes a new FPGA architecture that accelerates the circuit simulation and hence substantially speeds up the RL-based design method for power converters. Our new architecture supports all power electronic circuit converters and their variations. It substantially improves the training speed of RL-based design methods. High-level synthesis (HLS) was used to build the accelerator on Amazon Web Service (AWS) F1 instance. An AWS virtual PC hosts the training algorithm. The host interacts with the FPGA accelerator by updating the circuit parameters, initiating simulation, and collecting the simulation results during training iterations. A script was created on the host side to facilitate this design method to convert a netlist containing circuit topology and parameters into core matrices in the FPGA accelerator. Experimental results showed 60× overall speedup of our RL-based design method in comparison with using a popular commercial simulator, PowerSim. 

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