Abstract: A circuit implementing a spiking neural network that includes a learning component that can learn from temporal correlations in the spikes regardless of correlations in the rates. In some embodiments, the learning component comprises a rate-discounting component. In some embodiments, the learning rule computes a rate-normalized covariance (normcov) matrix, detects clusters in this matrix, and sets the synaptic weights according to these clusters. In some embodiments, a synapse with a long-term plasticity rule has an efficacy that is composed by a weight and a fatiguing component. In some embodiments, A Hebbian plasticity component modifies the weight component and a short-term fatigue plasticity component modifies the fatiguing component. The fatigue component increases with increases in the presynaptic spike rate. In some embodiments, the fatigue component increases are implemented in a spike-based manner.

Abstract: The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample; scanning said charged particle beam over said sample; and detecting, using a first detector, emissions of a first type from the sample in response to the beam scanned over the sample. Spectral information of detected emissions of the first type is used for assigning a plurality of mutually different phases to said sample. In a further step, a corresponding plurality of different color hues—with reference to an HSV color space—are associated to said plurality of mutually different phases. Using a second detector, emissions of a second type from the sample in response to the beam scanned over the sample are detected. Finally an image representation of said sample is provided.

Abstract: Method and system are disclosed for determining sample composition from spectral data acquired by a charged particle microscopy system. Chemical elements in a sample are identified by processing the spectral data with a trained neural network (NN). If the identified chemical elements not matching with a known elemental composition of the sample, the trained NN is retrained with the spectral data and the known elemental composition of the sample. The retrained NN can then be used to identify chemical elements within other samples.

Abstract: Methods and systems for high dynamic range (HDR) charged particle analysis using clustering processes include accessing a plurality of instances of image data for a region of a sample, where each instance of image data was captured with different parameters by a charged particle microscope. The difference in parameters may include one or more of a difference in contrast, brightness, beam strength, beam type, gamma, and/or a combination thereof. An HDR charged particle microscopy data structure is then generated using the captured image data, and one or more features of the sample are identified based on the HDR charged particle microscopy data structure. In some methods according to the present invention, the methods further include performing EDS analysis on the sample based on the determined one or more identified features.

Abstract: A circuit implementing a spiking neural network that includes a learning component that can learn from temporal correlations in the spikes regardless of correlations in the rates. In some embodiments, the learning component comprises a rate-discounting component. In some embodiments, the learning rule computes a rate-normalized covariance (normcov) matrix, detects clusters in this matrix, and sets the synaptic weights according to these clusters. In some embodiments, a synapse with a long-term plasticity rule has an efficacy that is composed by a weight and a fatiguing component. In some embodiments, A Hebbian plasticity component modifies the weight component and a short-term fatigue plasticity component modifies the fatiguing component. The fatigue component increases with increases in the presynaptic spike rate. In some embodiments, the fatigue component increases are implemented in a spike-based manner.

Abstract: A circuit implementing a spiking neural network that includes a learning component that can learn from temporal correlations in the spikes regardless of correlations in the rates. In some embodiments, the learning component comprises a rate-discounting component. In some embodiments, the learning rule computes a rate-normalized covariance (normcov) matrix, detects clusters in this matrix, and sets the synaptic weights according to these clusters. In some embodiments, a synapse with a long-term plasticity rule has an efficacy that is composed by a weight and a fatiguing component. In some embodiments, A Hebbian plasticity component modifies the weight component and a short-term fatigue plasticity component modifies the fatiguing component. The fatigue component increases with increases in the presynaptic spike rate. In some embodiments, the fatigue component increases are implemented in a spike-based manner.

Abstract: A circuit implementing a spiking neural network that includes a learning component that can learn from temporal correlations in the spikes regardless of correlations in the rates. In some embodiments, the learning component comprises a rate-discounting component. In some embodiments, the learning rule computes a rate-normalized covariance (normcov) matrix, detects clusters in this matrix, and sets the synaptic weights according to these clusters. In some embodiments, a synapse with a long-term plasticity rule has an efficacy that is composed by a weight and a fatiguing component. In some embodiments, A Hebbian plasticity component modifies the weight component and a short-term fatigue plasticity component modifies the fatiguing component. The fatigue component increases with increases in the presynaptic spike rate. In some embodiments, the fatigue component increases are implemented in a spike-based manner.

Abstract: Artificial neuron apparatus includes first and second resistive memory cells. The first resistive memory cell is connected in first circuitry having a first input and output. The second resistive memory cell is connected in second circuitry having a second input and output. The first and second circuitry are operable in alternating read and write phases to apply a programming current to their respective memory cells on receipt of excitatory and inhibitory neuron input signals, respectively. During the write phase, resistance of the respective cells is changed in response to successive excitatory and inhibitory neuron input signals. During the read phase, a read current is applied to their respective cells to produce first and second measurement signals, respectively. An output circuit connected to the first and second outputs produces a neuron output signal at a neuron output when a difference between the first and second measurement signals traverses a threshold.

Abstract: The disclosure relates to a method of examining a sample using a charged particle microscope. The method comprises the steps of detecting using a first detector emissions of a first type from the sample in response to the beam scanned over the area of the sample. Then, using spectral information of detected emissions of the first type, at least a part of the scanned area of the sample is divided into multiple segments. According to the disclosure, emissions of the first type at different positions along the scan in at least one of said multiple segments may be combined to produce a combined spectrum of the sample in said one of said multiple segments. In an embodiment, a second detector is used to detect emissions of a second type, and this is used to divide the area of the sample into multiple regions. The first detector may be an EDS, and the second detector may be based on EM. This way, EDS data and EM data can be effectively combined for producing colored images.

Abstract: A method and system providing a multi-memristive synaptic element for a cognitive computing system. The multi-memristive synaptic element comprises an array of memristive devices. The method comprises arbitrating a synaptic weight allocation, a related synaptic weight being represented by a synaptic weight variable of said multi-memristive synaptic element, updating said synaptic weight variable by a delta amount, and assigning said memristive devices to elements of a clock-like ordered circular list for selecting a particular memristor of said memristive devices requiring to be updated by a deterministic, periodic global clock that points to a different memristor at every clock tick, such that said multi-memristive synaptic element has a larger dynamic range and a more linear conductance response than a single memristor synaptic element.

Abstract: A resistive memory cell is connected in circuitry which has a first input terminal for applying neuron input signals including a read portion and a write portion. The circuitry includes a read circuit producing a read signal dependent on resistance of the memory cell, and an output terminal providing a neuron output signal, dependent on the read signal in a first state of the memory cell. The circuitry also includes a storage circuit storing a measurement signal dependent on the read signal, and a switch set operable to supply the read signal to the storage circuit during application of the read portion of each neuron input signal to the memory cell, and, after application of the read portion, to apply the measurement signal in the apparatus to enable resetting of the memory cell to a second state.

Abstract: The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample; scanning said charged particle beam over said sample; and detecting, using a first detector, emissions of a first type from the sample in response to the beam scanned over the sample. Spectral information of detected emissions of the first type is used for assigning a plurality of mutually different phases to said sample. In a further step, a corresponding plurality of different color hues—with reference to an HSV color space—are associated to said plurality of mutually different phases. Using a second detector, emissions of a second type from the sample in response to the beam scanned over the sample are detected. Finally an image representation of said sample is provided.

Abstract: The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample; scanning said charged particle beam over said sample at a plurality of sample locations; and detecting, using a first detector, emissions of a first type from the sample in response to the beam scanned over the plurality of sample locations. Spectral information of detected emissions of the first type is used to assign a plurality of mutually different phases to said sample at said plurality of sample locations. Information relating to at least one previously assigned phase and its respective sample location is used for establishing an estimated phase for at least one other of the plurality of sample locations. Said estimated phase is assigned to said other sample location. A control unit is used to provide a data representation of said sample containing at least information on said plurality of sample locations and said phases.

Abstract: The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample, and scanning said charged particle beam over at least part of said sample. A first detector is used for obtaining measured detector signals corresponding to emissions of a first type from the sample at a plurality of sample positions. According to the method, a set of data class elements is provided, wherein each data class element relates an expected detector signal to a corresponding sample information value. The measured detector signals are processed, and processing comprises comparing said measured detector signals to said set of data class elements; determining at least one probability that said measured detector signals belong to a certain one of said set of data class elements; and assigning at least one sample information value and said at least one probability to each of the plurality of sample positions.

Abstract: The invention relates to a method of examining a sample using a charged particle microscope, comprising the steps of providing a charged particle beam, as well as a sample, and scanning said charged particle beam over said sample. A first detector is used for detecting emissions of a first type from the sample in response to the beam scanned over the sample. Using spectral information of detected emissions of the first type, a plurality of mutually different phases are assigned to said sample. An image representation of said sample is provided, wherein said image representation contains different color hues. The color hues are selected from a pre-selected range of consecutive color hues in such a way that the selected color hues comprise mutually corresponding intervals within said pre-selected range of consecutive color hues.

Abstract: This invention relates to an apparatus, system, and method for computing with neuromorphic circuit architectures that have neurons with interconnected internal state information. The interconnected internal state information allows the neurons to enable or strengthen the input to other neurons. Furthermore, neuron internal state information provides insights on the characteristics of the input data that can be used to enhance the performance of the neuromorphic system. The neuromorphic system can be implemented with an artificial phase-change-based neurons.

Abstract: The disclosure relates to a method of examining a sample using a charged particle microscope. The method comprises the steps of detecting using a first detector emissions of a first type from the sample in response to the beam scanned over the area of the sample. Then, using spectral information of detected emissions of the first type, at least a part of the scanned area of the sample is divided into multiple segments. According to the disclosure, emissions of the first type at different positions along the scan in at least one of said multiple segments may be combined to produce a combined spectrum of the sample in said one of said multiple segments. In an embodiment, a second detector is used to detect emissions of a second type, and this is used to divide the area of the sample into multiple regions. The first detector may be an EDS, and the second detector may be based on EM. This way, EDS data and EM data can be effectively combined for producing colored images.

Abstract: A sensor arrangement for position sensing comprises a magnetic field source and a magnetoresistive element arranged in a magnetic field generated by the magnetic field source, which magnetoresistive element provides an output signal (R) dependent on a position (x) of the magnetoresistive element relative to the magnetic field source. A feedback controller is configured to receive the output signal (R) of the magnetoresistive element and is configured to adjust one or more of the position (x) of the magnetoresistive element relative to the magnetic field source and a strength of the magnetic field generated by the magnetic field source acting on the magnetoresistive element dependent on the output signal (R) of the magnetoresistive element.

Abstract: A resistive memory cell is connected in circuitry which has a first input terminal for applying neuron input signals including a read portion and a write portion. The circuitry includes a read circuit producing a read signal dependent on resistance of the memory cell, and an output terminal providing a neuron output signal, dependent on the read signal in a first state of the memory cell. The circuitry also includes a storage circuit storing a measurement signal dependent on the read signal, and a switch set operable to supply the read signal to the storage circuit during application of the read portion of each neuron input signal to the memory cell, and, after application of the read portion, to apply the measurement signal in the apparatus to enable resetting of the memory cell to a second state.

Abstract: Artificial neuron apparatus includes first and second resistive memory cells. The first resistive memory cell is connected in first circuitry having a first input and output. The second resistive memory cell is connected in second circuitry having a second input and output. The first and second circuitry are operable in alternating read and write phases to apply a programming current to their respective memory cells on receipt of excitatory and inhibitory neuron input signals, respectively. During the write phase, resistance of the respective cells is changed in response to successive excitatory and inhibitory neuron input signals. During the read phase, a read current is applied to their respective cells to produce first and second measurement signals, respectively. An output circuit connected to the first and second outputs produces a neuron output signal at a neuron output when a difference between the first and second measurement signals traverses a threshold.