Multi-directionally wrinkle-able textile OLEDs for clothing-type displays

A clothing-type wearable display can be utilized in fashion,bio-healthcare,and safety industries as well as smart textiles for the internet of things(IoTs)and wearable devices.In response to this trend,we demonstrate a textile display that can endure the active movements of a human body.It can be applied to any kind of textile,and is durable against conditions such as rain,sweat,and washing.As a key technology for realizing the multi-directional wrinkle-able textile display,we fabricated a stress-lowering textile platform with an ultrathin planarization layer replicated from the flat surface of glass.An elastomeric strain buffer for reducing mechanical stress is also inserted into the textile platform.Here,organic light-emitting diodes(OLEDs)with red,green and blue color,thin film transistors(TFTs)fabricated at a low temperature below 150℃,and a washable encapsulation layer blocking both gas and liquid were demonstrated on the textile platform.

Non-destructive thickness characterisation of 3D multilayer semiconductor devices using optical spectral measurements and machine learning

Three-dimensional(3D)semiconductor devices can address the limitations of traditional two-dimensional(2D)devices by expanding the integration space in the vertical direction.A 3D NOT-AND(NAND)flash memory device is presently the most commercially successful 3D semiconductor device.It vertically stacks more than 100 semiconductor material layers to provide more storage capacity and better energy efficiency than 2D NAND flash memory devices.In the manufacturing of 3D NAND,accurate characterisation of layer-by-layer thickness is critical to prevent the production of defective devices due to non-uniformly deposited layers.To date,electron microscopes have been used in production facilities to characterise multilayer semiconductor devices by imaging cross-sections of samples.However,this approach is not suitable for total inspection because of the wafer-cutting procedure.Here,we propose a non-destructive method for thickness characterisation of multilayer semiconductor devices using optical spectral measurements and machine learning.For>200-layer oxide/nitride multilayer stacks,we show that each layer thickness can be non-destructively determined with an average of approximately 1.6Åroot-mean-square error.We also develop outlier detection models that can correctly classify normal and outlier devices.This is an important step towards the total inspection of ultra-high-density 3D NAND flash memory devices.It is expected to have a significant impact on the manufacturing of various multilayer and 3D devices.