Abstract: The disclosure relates to methods for forming MmWave RF (MMW) circuits in multilayered Additively Manufactured Electronics (AME). Specifically, the disclosure relates to methods for direct and/or indirect inkjet printing of reconfigurable and/or tunable RF and/or MMW AME circuit applications.

Abstract: A multi-sequence transformer predict N tokens, in parallel, for log files in industrial machines. Token patterns derived from N log file token sequences logged by N respective loggers may be input into N respective intra sequence multi-head self-attention layers identifying patterns among tokens within the same log file token sequence generated by the same logger. Token patterns derived from a combination of the N log file token sequences may be input into a same inter sequence multi-head self-attention layer identifying patterns among tokens across multiple different sequences generated by multiple different loggers. N softmax layers may be generated of N distinct probability distributions that each candidate tokens is a next token in each of the N respective log file token sequences. A plurality of N next tokens may be predicted, in parallel, to co-occur in the plurality of N respective sequences of log file tokens.

Abstract: An efficient technique of machine learning is provided for training a plurality of convolutional neural networks (CNNs) with increased speed and accuracy using a genetic evolutionary model. A plurality of artificial chromosomes may be stored representing weights of artificial neuron connections of the plurality of respective CNNs. A plurality of pairs of the chromosomes may be recombined to generate, for each pair, a new chromosome (with a different set of weights than in either chromosome of the pair) by selecting entire filters as inseparable groups of a plurality of weights from each of the pair of chromosomes (e.g., “filter-by-filter” recombination). A plurality of weights of each of the new or original plurality of chromosomes may be mutated by propagating recursive error corrections incrementally throughout the CNN. A small random sampling of weights may optionally be further mutated to zero, random values, or a sum of current and random values.

Abstract: A device, system, method and non-transitory computer-readable storage medium for filtering a dataset for training a neural network. Initial instances of recorded input samples of source recording device(s) may be encoded, according to an input map, from the training dataset to respective nodes in the neural network's input layer. The input layer of the neural network may be sparsified by eliminating its nodes during a training phase. The training dataset may be filtered to exclude subsequent instances of recorded input samples from the source recording devices encoded in the eliminated nodes. Subsequent instances of the recorded input samples of the filtered training dataset may be encoded to remaining nodes not eliminated (and not to eliminated nodes) in the input layer to train the neural network in a subsequent training phase or generate a prediction output of the neural network in a prediction phase.

Abstract: The disclosure relates to systems and methods for fabricating passive RC frequency filter. More specifically, the disclosure is directed to computerized systems and methods for using additive manufacturing (AM) of simultaneous deposition of conductive and dielectric inks to form passive RC frequency pass filters having predetermined cutoff frequency with wide stop band frequency.

Abstract: Methods of inspecting a product made by additive manufacturing (AM) of multiple layers, computer program products and inspection modules for AM systems are provided. An augmented file is derived from a design file including layer data used to produce the product by AM. For each design layer, the augmented file includes the layer data for the design layer and weighted layer data for design layers beneath the design layer. A machine learning (ML) algorithm (trained on previous images and augmented files) is applied with respect to the derived augmented file onto received optical inspection images of the product during the AM process to detect production errors.

Abstract: Methods, computer program products and nozzle monitoring modules are provided, to monitor nozzles in 2D and/or 3D inkjet printing systems. Nozzle monitoring comprises registering an image of a printed product with respect to a corresponding raster file and evaluating nozzle performance and managing nozzles by applying a neural network (NN) trained on a plurality of registered images of the printed product and corresponding raster files. The disclosed NN approach may apply a deep learning model, and has been shown to improve performance over manual analysis of images of printed products (which is the current method of monitoring nozzles). Disclosed methods and modules may be implemented in 2D inkjet printing system and/or in 3D additive manufacturing (AM) inkjet printing system, monitoring nozzles that deposit layers of the 3D product.

Abstract: The disclosure relates to structures of, and methods for forming electromagnetic band gap (EBG) element. Specifically, the disclosure is directed to methods for additively manufacturing electronic mushroom-type EBG elements having a periodic cell structure enabling a reduced footprint and increased band gap range for a very wide range of frequencies, for example between 500 MHz to about 30 GHz, by altering both the EBG structure's superstrate as well as the ground plane.

Abstract: A device, system, and method is provided to mimic a pre-trained target model without access to the pre-trained target model or its original training dataset. A set of random or semi-random input data may be sent to randomly probe the pre-trained target model at a remote device. A set of corresponding output data may be received from the remote device that is generated by applying the pre-trained target model to the set of random or semi-random input data. A random probe training dataset may be generated comprising the set of random or semi-random input data and corresponding output data generated by randomly probing the pre-trained target model. A new model may be trained with the random probe training dataset so that the new model generates substantially the same corresponding output data in response to said input data to mimic the pre-trained target model.

Abstract: An efficient technique of machine learning is provided for training a plurality of convolutional neural networks (CNNs) with increased speed and accuracy using a genetic evolutionary model. A plurality of artificial chromosomes may be stored representing weights of artificial neuron connections of the plurality of respective CNNs. A plurality of pairs of the chromosomes may be recombined to generate, for each pair, a new chromosome (with a different set of weights than in either chromosome of the pair) by selecting entire filters as inseparable groups of a plurality of weights from each of the pair of chromosomes (e.g., “filter-by-filter” recombination). A plurality of weights of each of the new or original plurality of chromosomes may be mutated by propagating recursive error corrections incrementally throughout the CNN. A small random sampling of weights may optionally be further mutated to zero, random values, or a sum of current and random values.

Abstract: An efficient technique of machine learning is provided for training a plurality of convolutional neural networks (CNNs) with increased speed and accuracy using a genetic evolutionary model. A plurality of artificial chromosomes may be stored representing weights of artificial neuron connections of the plurality of respective CNNs. A plurality of pairs of the chromosomes may be recombined to generate, for each pair, a new chromosome (with a different set of weights than in either chromosome of the pair) by selecting entire filters as inseparable groups of a plurality of weights from each of the pair of chromosomes (e.g., “filter-by-filter” recombination). A plurality of weights of each of the new or original plurality of chromosomes may be mutated by propagating recursive error corrections incrementally throughout the CNN. A small random sampling of weights may optionally be further mutated to zero, random values, or a sum of current and random values.

Abstract: The disclosure relates to methods for fabricating coreless printed circuit board (PCB) based transformers and/or coreless PCB-based circuits containing one or more coil inductor(s). More specifically, the disclosure relates to methods for fabricating coreless PCB-based transformers and/or inductors having concatenated helix architecture of their primary and secondary windings using layer-by-layer printing of dielectric and conductive patterns.

Abstract: A device, system, and method is provided for storing a sparse neural network. A plurality of weights of the sparse neural network may be obtained. Each weight may represent a unique connection between a pair of a plurality of artificial neurons in different layers of a plurality of neuron layers. A minority of pairs of neurons in adjacent neuron layers are connected in the sparse neural network. Each of the plurality of weights of the sparse neural network may be stored with an association to a unique index. The unique index may uniquely identify a pair of artificial neurons that have a connection represented by the weight. Only non-zero weights may be stored that represent connections between pairs of neurons (and zero weights may not be stored that represent no connections between pairs of neurons).

Abstract: The disclosure relates to systems, methods and compositions for fabricating additive manufactured electronics having conductive and dielectric constituents comprising voids, using additive manufacturing. Specifically, the disclosure is directed to the fabrication of three-dimensional component having conductive and dielectric constituents comprising voids by using water soluble support ink, capable of undergoing all processing steps for fabricating the dielectric and conductive constituents.

Abstract: A device, system, and method is provided for storing a sparse neural network. A plurality of weights of the sparse neural network may be obtained. Each weight may represent a unique connection between a pair of a plurality of artificial neurons in different layers of a plurality of neuron layers. A minority of pairs of neurons in adjacent neuron layers are connected in the sparse neural network. Each of the plurality of weights of the sparse neural network may be stored with an association to a unique index. The unique index may uniquely identify a pair of artificial neurons that have a connection represented by the weight. Only non-zero weights may be stored that represent connections between pairs of neurons (and zero weights may not be stored that represent no connections between pairs of neurons).

Abstract: The disclosure relates to systems, methods and compositions for fabricating composite component using additive manufacturing (AM). Specifically, the disclosure is directed to methods, systems and compositions for the fabrication of composite components having improved or modulated thermo-mechanical properties, as well as derivative dielectric strength, using for example, inkjet printing.

Abstract: The disclosure relates to systems and methods for using additive manufacturing techniques for fabricating ball grid array (BGA) surface mounting pads (SMP), and surface mounted technology devices (SMT) package sockets. More specifically, the disclosure relates to additive manufacturing methods for additively manufactured electronic (AME) circuits such as a printed circuit board (PCB), and/or flexible printed circuit (FPC), and/or high-density interconnect printed circuit board (HDIPCB) each having integrated raised and/or sunk BGA SMP, and or surface mounting sockets for SMT device(s) defined therein, and methods of coupling surface mounted devices such as BGA and/or SMT thereto.

Abstract: The disclosure relates to methods and compositions for direct printing of composite components. Specifically, the disclosure relates to the continuous printing of colored resin and metallic composite components using inkjet printing.

Abstract: The disclosure relates to a portable system for cleaning ink jet print heads. Specifically, the disclosure relates to external, portable systems and jig assemblies for cleaning clogged nozzles of ink jet print head under controlled pressure. The jig assembly can be self-contained and detachable, and can be programmed to operate upon receiving various signals indicating clogged nozzle plate or portion thereof, and/or as a routine maintenance operating procedure.

Abstract: The disclosure relates to systems and methods for photonic sintering of conductive ink compositions with metal nanoparticles. Specifically, the disclosure relates to methods and systems for sintering ink compositions with metal nanoparticles using an illumination source comprising an array of pulsed light emitting diodes (LEDs).