Objective: To improve the existing manually-assembled cochlear implant electrode arrays, a thin film electrode array (TFEA) was micro-fabricated to have a maximum electrode density of 15 sites along an 8-mm length, with each site having a 751.8-μm (diameter - height) disk electrode. Methods: The micro-fabrication method adopted a sequence of photoresist transferring, lift-off, two-step oxygen plasma etching, and fuming nitric acid release to reduce lift-off complexity, protect the metal layer, and increase the release efficiency. Results: Systematic in vitro characterization showed that the TFEA's bending stiffness was 6.4010Nμmnear the base and 1.2610Nμmnear the apex. On average, the TFEA electrode produced an impedance of 16 kμ and a maximum current limit of 800 μA, measured with a 1-kHz sinusoidal current using monopolar stimulation in saline. A TFEA prototype was implanted in a cat cochlea to obtain in vivo measurements of electrically-evoked auditory brainstem and inferior colliculus responses to monopolar stimulation with 41-μs/phase, biphasic pulses. Both physiological responses produced a threshold of ~300 μA and a dynamic range of 58 dB above the threshold. Comparing with existing electrode arrays, the present TFEA had 104 times less bending stiffness, 97% less electrode area, and comparable physiological thresholds. Conclusion: Using a simplified structure and stable fabrication method, the present TEFA produced physical and physiological performance comparable to existing commercial devices. Significance: The present TFEA and micro-fabrication method represent a step closer towards an automated process replacing the labor-intensive and expensive manual assembly of electrode arrays in most neural prostheses including the cochlear implant.

The micro-fabricated thin film electrode array (TFEA) has been a promising design for cochlear implants (CIs) because of its cost-effectiveness and fabrication precision. The latest polymer-based cochlear TFEAs have faced difficulties for cochlear insertion due to the lack of structural stiffness. To stiffen the TFEA, dissolvable stiffening materials, TFEAs with different structures, and TFEAs with commercial CIs as carriers have been invested. In this work, the concept of enhancing a Parylene TFEA with Kapton tape as a simpler carrier for cochlear insertion has been proved to be feasible. The bending stiffness of the Kapton-aided TFEA was characterized with an analytical model, a finite element model, and a cantilever bending experiment, respectively. While the Kapton tape increased the bending stiffness of the Parylene TFEA by 10³ times, the 6-μm-thick TFEA with a similar Young’s modulus, as a polyimide, in turn significantly increased the bending stiffness of the 170-μm-thick Kapton carrier by 60%. This result indicated that even the TFEA is ultra-flexible and that its bending stiffness should not be neglected in the design or selection of its carrier.

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We firstly maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively.

Morphology control can enable multifunction micro devices, but the fabrication of 3D structures is challenging. This paper presents a planar fabrication compatible pneumatic actuator based on corrugation structure. The actuator exhibits large deformation and high actuation force on sub-millimeter scale. Furthermore, the deformation is self-sustainable after actuation, and the deformation morphology is programable by tuning the actuator pattern. The actuator enables a novel access to achieve 3D morphology control in micro-devices.