Effect of Substrate Holder Design on Stress and Uniformity of Large-Area Polycrystalline Diamond Films Grown by Microwave Plasma-Assisted CVD

In this work, the substrate holders of three principal geometries (flat, pocket, and pedestal) were designed based on E-field simulations. They were fabricated and then tested in microwave plasma-assisted chemical vapor deposition process with the purpose of the homogeneous growth of 100-μm-thick, low-stress polycrystalline diamond film over 2-inch Si substrates with a thickness of 0.35 mm. The effectiveness of each holder design was estimated by the criteria of the PCD film quality, its homogeneity, stress, and the curvature of the resulting “diamond-on-Si” plates. The structure and phase composition of the synthesized samples were studied with scanning electron microscopy and Raman spectroscopy, the curvature was measured using white light interferometry, and the thermal conductivity was measured using the laser flash technique. The proposed pedestal design of the substrate holder could reduce the stress of the thick PCD film down to 1.1–1.4 GPa, which resulted in an extremely low value of displacement for the resulting “diamond-on-Si” plate of Δh = 50 μm. The obtained results may be used for the improvement of already existing, and the design of the novel-type, MPCVD reactors aimed at the growth of large-area thick homogeneous PCD layers and plates for electronic applications

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)<i>_x</i>(LiNbO₃)_{100−<i>x</i>} Nanocomposite Memristors

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements