Advances in Reliability Monitoring and Failure Analysis in 3D Microsystems
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Jordan, Matthew↗
Engineering topics
Lee, Jaesung
Publications and source records attributed to Lee, Jaesung.
Fully Inkjet‐Printed, 2D Materials‐Based Field‐Effect Transistor for Water Sensing
Abstract Despite significant progress in solution‐processing of 2D materials, it remains challenging to reliably print high‐performance semiconducting channels that can be efficiently modulated in a field‐effect transistor (FET). Herein, electrochemically exfoliated MoS 2 nanosheets are inkjet‐printed into ultrathin semiconducting channels, resulting in high on/off current ratios up to 10 3 . The reported printing strategy is reliable and general for thin film channel fabrication even in the presence of the ubiquitous coffee‐ring effect. Statistical modeling analysis on the printed pattern profiles suggests that a spaced parallel printing approach can overcome the coffee‐ring effect during inkjet printing, resulting in uniform 2D flake percolation networks. The uniformity of the printed features allows the MoS 2 channel to be hundreds of micrometers long, which easily accommodates the typical inkjet printing resolution of tens of micrometers, thereby enabling fully printed FETs. As a proof of concept, FET water sensors are demonstrated using printed MoS 2 as the FET channel, and printed graphene as the electrodes and the sensing area. After functionalization of the sensing area, the printed water sensor shows a selective response to Pb 2+ in water down to 2 ppb. This work paves the way for additive nanomanufacturing of FET‐based sensors and related devices using 2D nanomaterials.
36 MATERIALS SCIENCE↗
Robust Parameter Design on Dual Stochastic Response Models With Constrained Bayesian Optimization
In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. Further, the performance of our proposed method is demonstrated by the extensive numerical and case studies. Note to Practitioners-Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.
42 ENGINEERING↗
Multiple Stable Oscillators Referenced to the Same Multimode AlN/Si MEMS Resonator with Mode-Dependent Phase Noise and Frequency Stability
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47 OTHER INSTRUMENTATION↗
Characterization of Plasma Synthesized Vertical Carbon Nanofibers for Nanoelectronics Applications
We report on the material characterization of carbon nanofibers (CNFs) which are assembled into a three-dimensional (3D) configuration for making new nanoelectromechanical systems (NEMS). High-resolution scanning electron microscopy (SEM) and x-ray electron dispersive spectroscopy (XEDS) are employed to decipher the morphology and chemical compositions of the CNFs at various locations along individual CNFs grown on silicon (Si) and refractory nitride (NbTiN) substrates, respectively. The measured characteristics suggest interesting properties of the CNF bodies and their capping catalyst nanoparticles, and growth mechanisms on the two substrates. Laser irradiation on the CNFs seems to cause thermal oxidation and melting of catalyst nanoparticles. The structural morphology and chemical compositions of the CNFs revealed in this study should aid in the applications of the CNFs to nanoelectronics and NEMS.
carbon nanomaterials↗