Insulator interface effects in sputter‐deposited NbN/MgO/NbN (superconductor–insulator–superconductor) tunnel junctions

All refractory, NbN/MgO/NbN (superconductor–insulator–superconductor) tunnel junctions have been fabricated by in situ sputter deposition. The influence of MgO thickness (0.8–6.0 nm) deposited under different sputtering ambients at various deposition rates on current–voltage (I–V) characteristics of small‐area (30×30 μm) tunnel junctions is studied. The NbN/MgO/NbN trilayer is deposited in situ by dc reactive magnetron (NbN), and rf magnetron (MgO) sputtering, followed by thermal evaporation of a protective Au cap. Subsequent photolithography, reactive ion etching, planarization, and top contact (Pb/Ag) deposition completes the junction structure. Normal resistance of the junctions with MgO deposited in Ar or Ar and N2 mixture shows good exponential dependence on the MgO thickness indicating formation of a pin‐hole‐free uniform barrier layer. Further, a postdeposition in situ oxygen plasma treatment of the MgO layer increases the junction resistance sharply, and reduces the subgap leakage. A possible enrichment of the MgO layer stoichiometry by the oxygen plasma treatment is suggested. A sumgap as high as 5.7 mV is observed for such a junctio

Corrosion resistant coating

A method of coating a substrate with an amorphous metal is described. A solid piece of the metal is bombarded with ions of an inert gas in the presence of a magnetic field to provide a vapor of the metal which is deposited on the substrate at a sufficiently low gas pressure so that there is formed on the substrate a thin, uniformly thick, essentially pinhole-free film of the metal

Thin-film chemical sensors based on electron tunneling

The physical mechanisms underlying a novel chemical sensor based on electron tunneling in metal-insulator-metal (MIM) tunnel junctions were studied. Chemical sensors based on electron tunneling were shown to be sensitive to a variety of substances that include iodine, mercury, bismuth, ethylenedibromide, and ethylenedichloride. A sensitivity of 13 parts per billion of iodine dissolved in hexane was demonstrated. The physical mechanisms involved in the chemical sensitivity of these devices were determined to be the chemical alteration of the surface electronic structure of the top metal electrode in the MIM structure. In addition, electroreflectance spectroscopy (ERS) was studied as a complementary surface-sensitive technique. ERS was shown to be sensitive to both iodine and mercury. Electrolyte electroreflectance and solid-state MIM electroreflectance revealed qualitatively the same chemical response. A modified thin-film structure was also studied in which a chemically active layer was introduced at the top Metal-Insulator interface of the MIM devices. Cobalt phthalocyanine was used for the chemically active layer in this study. Devices modified in this way were shown to be sensitive to iodine and nitrogen dioxide. The chemical sensitivity of the modified structure was due to conductance changes in the active layer

Optimal and Local Connectivity Between Neuron and Synapse Array in the Quantum Dot/Silicon Brain

This innovation is used to connect between synapse and neuron arrays using nanowire in quantum dot and metal in CMOS (complementary metal oxide semiconductor) technology to enable the density of a brain-like connection in hardware. The hardware implementation combines three technologies: 1. Quantum dot and nanowire-based compact synaptic cell (50x50 sq nm) with inherently low parasitic capacitance (hence, low dynamic power approx.l0(exp -11) watts/synapse), 2. Neuron and learning circuits implemented in 50-nm CMOS technology, to be integrated with quantum dot and nanowire synapse, and 3. 3D stacking approach to achieve the overall numbers of high density O(10(exp 12)) synapses and O(10(exp 8)) neurons in the overall system. In a 1-sq cm of quantum dot layer sitting on a 50-nm CMOS layer, innovators were able to pack a 10(exp 6)-neuron and 10(exp 10)-synapse array; however, the constraint for the connection scheme is that each neuron will receive a non-identical 10(exp 4)-synapse set, including itself, via its efficacy of the connection. This is not a fully connected system where the 100x100 synapse array only has a 100-input data bus and 100-output data bus. Due to the data bus sharing, it poses a great challenge to have a complete connected system, and its constraint within the quantum dot and silicon wafer layer. For an effective connection scheme, there are three conditions to be met: 1. Local connection. 2. The nanowire should be connected locally, not globally from which it helps to maximize the data flow by sharing the same wire space location. 3. Each synapse can have an alternate summation line if needed (this option is doable based on the simple mask creation). The 10(exp 3)x10(exp 3)-neuron array was partitioned into a 10-block, 10(exp 2)x10(exp 3)-neuron array. This building block can be completely mapped within itself (10,000 synapses to a neuron)

Cascaded VLSI neural network architecture for on-line learning

High-speed, analog, fully-parallel and asynchronous building blocks are cascaded for larger sizes and enhanced resolution. A hardware-compatible algorithm permits hardware-in-the-loop learning despite limited weight resolution. A comparison-intensive feature classification application has been demonstrated with this flexible hardware and new algorithm at high speed. This result indicates that these building block chips can be embedded as application-specific-coprocessors for solving real-world problems at extremely high data rates

Electronic neural network for solving traveling salesman and similar global optimization problems

This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically

Refractory amorphous metallic (W0.6Re0.4)76B24 coatings on steel substrates

Refractory metallic coatings of (W0.6Re0.4)76B24 (WReB) have been deposited onto glass, quartz, and heat-treated AISI 52100 bearing steel substrates by dc magnetron sputtering. As-deposited WReB films are amorphous, as shown by their diffuse x-ray diffraction patterns; chemically homogeneous, according to secondary ion mass spectrometry (SIMS) analysis; and they exhibit a very high (~1000°C) crystallization temperature. Adhesion strength of these coatings on heat-treated AISI 52100 steel is in excess of ~20, 000 psi and they possess high microhardness (~2400 HV50). Unlubricated wear resistance of such hard and adherent amorphous metallic coatings on AISI 52100 steel is studied using the pin-on-disc method under various loading conditions. Amorphous metallic WReB coatings, about 4 µm thick, exhibit an improvement of more than two and a half orders of magnitude in the unlubricated wear resistance over that of the uncoated AISI 52100 steel

Shape recognition through multi-level fusion of features and classifiers

Shape recognition is a fundamental problem and a special type of image classification, where each shape is considered as a class. Current approaches to shape recognition mainly focus on designing low-level shape descriptors, and classify them using some machine learning approaches. In order to achieve effective learning of shape features, it is essential to ensure that a comprehensive set of high quality features can be extracted from the original shape data. Thus we have been motivated to develop methods of fusion of features and classifiers for advancing the classification performance. In this paper, we propose a multi-level framework for fusion of features and classifiers in the setting of gran-ular computing. The proposed framework involves creation of diversity among classifiers, through adopting feature selection and fusion to create diverse feature sets and to train diverse classifiers using different learn-Xinming Wang algorithms. The experimental results show that the proposed multi-level framework can effectively create diversity among classifiers leading to considerable advances in the classification performance