In this paper we describe a method for controlling both the residual stress and the through-thickness stress gradient of aluminum nitride (AlN) thin films using a multi-step deposition process that varies the applied radio frequency (RF) substrate bias. The relationship between the applied RF substrate bias and the AlN residual stress is characterized using AlN films grown on oxidized silicon substrates is determined using 100 nm–1.5 μm thick blanket AlN films that are deposited with 60–100 W applied RF biases; the stress-bias relationship is found to be well described using a power law relationship. Using this relationship, we develop a model for varying the RF bias in a series of discrete deposition steps such that each deposition step has zero average stress. The applied RF bias power in these steps is tailored to produce AlN films that have minimized both the residual stress and the film stress gradient. AlN cantilevers were patterned from films deposited using this technique, which show reduced curvature compared to those deposited using a single RF bias setting, indicating a reduction of the stress gradient in the films.

Objective: We sought to determine whether electrical patterns in endocardial wavefronts contained elements specific to atrial fibrillation (AF) disease progression. Methods: A canine paced model (n=7, female mongrel, 29±2 kg) of persistent AF was endocardially mapped with a 64-electrode basket catheter during periods of AF at 1 month, 3 month, and 6 months post-implant of stimulator. A 50-layer residual network was then trained to map halfsecond electrogram samples to their source timepoint. Results: The trained network achieved final validation and testing accuracies of 51.6 and 48.5% respectively. Per class F1 scores were 24%, 59%, and 53% for 1 month, 3 month, and 6 month inputs from the testing dataset. Conclusion: Differentiation of AF based on its time progression was shown to be feasible with a deep learning method. This is promising for differentiating treatment based on disease progression though low accuracy with earlier timepoints may be an obstacle to identifying nascent AF.

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