Patents Assigned to Nano-Dimension Technologies Ltd.
  • Patent number: 12290990
    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.
    Type: Grant
    Filed: November 22, 2021
    Date of Patent: May 6, 2025
    Assignee: Nano Dimension Technologies, LTD
    Inventor: Minoru Yamada
  • Publication number: 20250077950
    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.
    Type: Application
    Filed: August 30, 2023
    Publication date: March 6, 2025
    Applicant: Nano Dimension Technologies, Ltd.
    Inventors: Eli DAVID, Eri RUBIN
  • Publication number: 20250061306
    Abstract: A device, system, and method for approximating a neural network comprising N synapses or filters. The neural network may be partially-activated by iteratively executing a plurality of M partial pathways of the neural network to generate M partial outputs, wherein the M partial pathways respectively comprise M different continuous sequences of synapses or filters linking an input layer to an output layer. The M partial pathways may cumulatively span only a subset of the N synapses or filters such that a significant number of the remaining the N synapses or filters are not computed. The M partial outputs of the M partial pathways may be aggregated to generate an aggregated output approximating an output generated by fully-activating the neural network by executing a single instance of all N synapses or filters of the neural network. Training or prediction of the neural network may be performed based on the aggregated output.
    Type: Application
    Filed: October 30, 2024
    Publication date: February 20, 2025
    Applicant: NANO DIMENSION TECHNOLOGIES, LTD.
    Inventors: Eli DAVID, Eri RUBIN
  • Patent number: 12229650
    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).
    Type: Grant
    Filed: February 13, 2023
    Date of Patent: February 18, 2025
    Assignee: NANO DIMENSION TECHNOLOGIES, LTD.
    Inventors: Eli David, Eri Rubin
  • Publication number: 20250053816
    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.
    Type: Application
    Filed: October 30, 2024
    Publication date: February 13, 2025
    Applicant: Nano Dimension Technologies, Ltd.
    Inventor: Eli DAVID
  • Publication number: 20250005349
    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.
    Type: Application
    Filed: June 29, 2023
    Publication date: January 2, 2025
    Applicant: Nano Dimension Technologies, Ltd.
    Inventors: Dany ROVINSKY, Eri RUBIN, Eli DAVID
  • Patent number: 12147903
    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.
    Type: Grant
    Filed: July 24, 2023
    Date of Patent: November 19, 2024
    Assignee: NANO DIMENSION TECHNOLOGIES, LTD.
    Inventor: Eli David
  • Patent number: 12141678
    Abstract: A device, system, and method for approximating a neural network comprising N synapses or filters. The neural network may be partially-activated by iteratively executing a plurality of M partial pathways of the neural network to generate M partial outputs, wherein the M partial pathways respectively comprise M different continuous sequences of synapses or filters linking an input layer to an output layer. The M partial pathways may cumulatively span only a subset of the N synapses or filters such that a significant number of the remaining the N synapses or filters are not computed. The M partial outputs of the M partial pathways may be aggregated to generate an aggregated output approximating an output generated by fully-activating the neural network by executing a single instance of all N synapses or filters of the neural network. Training or prediction of the neural network may be performed based on the aggregated output.
    Type: Grant
    Filed: December 28, 2020
    Date of Patent: November 12, 2024
    Assignee: NANO DIMENSION TECHNOLOGIES, LTD.
    Inventors: Eli David, Eri Rubin
  • Patent number: 12133336
    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.
    Type: Grant
    Filed: June 24, 2021
    Date of Patent: October 29, 2024
    Assignee: Nano Dimension Technologies, LTD.
    Inventor: Daniel Sokol
  • Publication number: 20240255916
    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.
    Type: Application
    Filed: January 26, 2023
    Publication date: August 1, 2024
    Applicant: Nano Dimension Technologies Ltd
    Inventors: Ben LEVY, Itay MOSAFI, Dany ROVINSKY, Eri RUBIN, Eli DAVID
  • Publication number: 20240257333
    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.
    Type: Application
    Filed: January 26, 2023
    Publication date: August 1, 2024
    Applicant: Nano Dimension Technologies Ltd
    Inventors: Eri RUBIN, Yotam Raz, Itay Mosafi, Marina Izmailov, Katia Huri, Eli David
  • Publication number: 20240232635
    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.
    Type: Application
    Filed: February 15, 2024
    Publication date: July 11, 2024
    Applicant: NANO DIMENSION TECHNOLOGIES, LTD.
    Inventor: Eli DAVID
  • Publication number: 20240220807
    Abstract: A device, system, and method is provided for training a new neural network to mimic a target neural network without access to the target neural network or its original training dataset. The target neural network and the new neural network may be probed with input data to generate corresponding target and new output data. Input data may be detected that generate a maximum or above threshold difference between the corresponding target and new output data. A divergent probe training dataset may be generated comprising the input data that generate the maximum or above threshold difference and the corresponding target output data. The new neural network may be trained using the divergent probe training dataset to generate the target output data. The new neural network may be iteratively trained using an updated divergent probe training dataset dynamically adjusted as the new neural network changes during training.
    Type: Application
    Filed: March 13, 2024
    Publication date: July 4, 2024
    Applicant: NANO DIMENSION TECHNOLOGIES, LTD.
    Inventors: Eli DAVID, Eri RUBIN
  • Patent number: 11961003
    Abstract: A device, system, and method is provided for training a new neural network to mimic a target neural network without access to the target neural network or its original training dataset. The target neural network and the new neural network may be probed with input data to generate corresponding target and new output data. Input data may be detected that generate a maximum or above threshold difference between the corresponding target and new output data. A divergent probe training dataset may be generated comprising the input data that generate the maximum or above threshold difference and the corresponding target output data. The new neural network may be trained using the divergent probe training dataset to generate the target output data. The new neural network may be iteratively trained using an updated divergent probe training dataset dynamically adjusted as the new neural network changes during training.
    Type: Grant
    Filed: July 8, 2020
    Date of Patent: April 16, 2024
    Assignee: NANO DIMENSION TECHNOLOGIES, LTD.
    Inventors: Eli David, Eri Rubin
  • Patent number: 11937380
    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.
    Type: Grant
    Filed: December 31, 2021
    Date of Patent: March 19, 2024
    Assignee: Nano Dimension Technologies, LTD.
    Inventor: Minoru Yamada
  • Patent number: 11907854
    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.
    Type: Grant
    Filed: June 24, 2020
    Date of Patent: February 20, 2024
    Assignee: Nano Dimension Technologies, Ltd.
    Inventor: Eli David
  • Publication number: 20230376777
    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.
    Type: Application
    Filed: July 24, 2023
    Publication date: November 23, 2023
    Applicant: Nano Dimension Technologies, Ltd.
    Inventor: Eli DAVID
  • Patent number: 11710044
    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.
    Type: Grant
    Filed: July 1, 2019
    Date of Patent: July 25, 2023
    Assignee: Nano Dimension Technologies, Ltd.
    Inventor: Eli David
  • Patent number: 11694837
    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.
    Type: Grant
    Filed: May 4, 2020
    Date of Patent: July 4, 2023
    Assignee: Nano Dimension Technologies, LTD.
    Inventor: Minoru Yamada
  • Publication number: 20230196061
    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).
    Type: Application
    Filed: February 13, 2023
    Publication date: June 22, 2023
    Applicant: Nano Dimension Technologies, Ltd.
    Inventors: Eli DAVID, Eri RUBIN