Abstract: There is provided a system and method for examining a semiconductor specimen. The method includes obtaining a runtime image of the specimen, and providing the runtime image as an input to an end-to-end (E2E) learning model to process, thereby obtaining, as an output of the E2E learning model, runtime measurement data specific for a metrology application. The E2E learning model is previously trained for the metrology application using a training set comprising a plurality of training images of the specimen and respective ground truth measurement data associated therewith, and one or more cost functions specifically configured to evaluate, for the plurality of training images and corresponding training measurement data outputted by the E2E learning model, one or more metrology benchmarks from a group comprising precision, correlation, and matching.

Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: A system and method are presented for controlling measurements of various sample's parameters. The system comprises a control unit configured as a computer system comprising data input and output utilities, memory, and a data processor, and being configured to communicate with a measured data provider to receive measured data indicative of measurements on the sample. The data processor is configured to perform model-based processing of the measured data utilizing at least one predetermined model, and determine, for each of one or more measurements of one or more parameters of interest of the sample, an estimated upper bound on an error value for the measurement individually, and generate output data indicative thereof.

Abstract: A system and methods for Advance Process Control (APC) in semiconductor manufacturing include: for each of a plurality of waiter sites, receiving a pre-process set of scatterometric training data, measured before implementation of a processing step, receiving a corresponding post-process set of scatterometric training data measured after implementation of the process step, and receiving a set of process control knob training data indicative of process control knob settings applied during implementation of the process step; and generating a machine learning model correlating variations in the pre-process sets of scatterometric training data and the corresponding process control knob training data with the corresponding post-process sets of scatterometric training data, to train the machine learning model to recommend changes to process control knob settings to compensate for variations in the pre-process scatterometric data.

Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: A system and method are presented for controlling measurements of various sample's parameters. The system comprises a control unit configured as a computer system comprising data input and output utilities, memory, and a data processor, and being configured to communicate with a measured data provider to receive measured data indicative of measurements on the sample. The data processor is configured to perform model-based processing of the measured data utilizing at least one predetermined model, and determine, for each of one or more measurements of one or more parameters of interest of the sample, an estimated upper bound on an error value for the measurement individually, and generate output data indicative thereof.

Abstract: Mixed mode constellation mapping to map a data block to a block of sub-carriers based on a configurable set of one or more constellation mapping schemes, and corresponding mixed mode least likelihood ratio (LLR) de-mapping based on the configurable set of one or more modulation schemes. The set may be configurable to include multiple modulation schemes to provide to a SEvSNR measure that is a non-weighted or weighted average of SEvSNR measures of the multiple modulation schemes. Mixed mode constellation mapping may be useful be configurable to control spectral efficiency versus SNR (SEvSNR) over a range of SNR with relatively fine SNR granularity, and may be configurable to control SEvSNR over a range of SNR at a fixed FEC code rate, which may include a highest available or highest permitted code rate.

Abstract: A system and methods for Advance Process Control (APC) in semiconductor manufacturing include: for each of a plurality of waiter sites, receiving a pre-process set of scatterometric training data, measured before implementation of a processing step, receiving a corresponding post-process set of scatterometric training data measured after implementation of the process step, and receiving a set of process control knob training data indicative of process control knob settings applied during implementation of the process step; and generating a machine learning model correlating variations in the pre-process sets of scatterometric training data and the corresponding process control knob training data with the corresponding post-process sets of scatterometric training data, to train the machine learning model to recommend changes to process control knob settings to compensate for variations in the pre-process scatterometric data.

Abstract: A cable modem system for discovering interference groups (IGs) includes an infrastructure and a cable modem termination system (CMTS). The infrastructure is for transferring data. The CMTS is configured to initiate generation of test signals by a set of cable modems (CMs), obtain a set of test measurements for the set of CMs, discover interference groups (IGs) of the set of CMs based on the obtained set of test measurements and assign a plurality of upstream and downstream channels for the set of CMs that use orthogonal frequency division multiplexing (OFDM) based on the discovered IGs.

Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: Mixed mode constellation mapping to map a data block to a block of sub-carriers based on a configurable set of one or more constellation mapping schemes, and corresponding mixed mode least likelihood ratio (LLR) de-mapping based on the configurable set of one or more modulation schemes. The set may be configurable to include multiple modulation schemes to provide to a SEvSNR measure that is a non-weighted or weighted average of SEvSNR measures of the multiple modulation schemes. Mixed mode constellation mapping may be useful be configurable to control spectral efficiency versus SNR (SEvSNR) over a range of SNR with relatively fine SNR granularity, and may be configurable to control SEvSNR over a range of SNR at a fixed FEC code rate, which may include a highest available or highest permitted code rate.

Abstract: A cable modem supporting full duplex (FDX) operations. The cable modem includes a transmit circuitry configured to process a transmit signal and a receive circuitry configured to process a receive signal. The receive circuitry includes a switchable analog filter configured to filter the receive signal. The switchable analog filter is configurable for different passband frequencies. The receive circuitry also includes a digital compensation filter configured to compensate a difference in frequency response in a specific frequency band due to switching of the switchable analog filter for a different passband frequency. The cable modem also includes an adjacent channel interference (ACI) cancellation filter and an adjacent leakage interference (ALI) cancellation filter. A digital compensation filter is also used in processing the ACI cancellation signal and the ALI cancellation signal to impose or compensate the difference in frequency response due to the switchable analog filter switching.

Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: Mixed mode constellation mapping to map a data block to a block of sub-carriers based on a configurable set of one or more constellation mapping schemes, and corresponding mixed mode least likelihood ratio (LLR) de-mapping based on the configurable set of one or more modulation schemes. The set may be configurable to include multiple modulation schemes to provide to a SEvSNR measure that is a non-weighted or weighted average of SEvSNR measures of the multiple modulation schemes. Mixed mode constellation mapping may be useful be configurable to control spectral efficiency versus SNR (SEvSNR) over a range of SNR with relatively fine SNR granularity, and may be configurable to control SEvSNR over a range of SNR at a fixed FEC code rate, which may include a highest available or highest permitted code rate.

Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

Abstract: Mixed mode constellation mapping to map a data block to a block of sub-carriers based on a configurable set of one or more constellation mapping schemes, and corresponding mixed mode least likelihood ratio (LLR) de-mapping based on the configurable set of one or more modulation schemes. The set may be configurable to include multiple modulation schemes to provide to a SEvSNR measure that is a non-weighted or weighted average of SEvSNR measures of the multiple modulation schemes. Mixed mode constellation mapping may be useful be configurable to control spectral efficiency versus SNR (SEvSNR) over a range of SNR with relatively fine SNR granularity, and may be configurable to control SEvSNR over a range of SNR at a fixed FEC code rate, which may include a highest available or highest permitted code rate.

Abstract: A cable modem system for discovering interference groups (IGs) includes an infrastructure and a cable modem termination system (CMTS). The infrastructure is for transferring data. The CMTS is configured to initiate generation of test signals by a set of cable modems (CMs), obtain a set of test measurements for the set of CMs, discover interference groups (IGs) of the set of CMs based on the obtained set of test measurements and assign a plurality of upstream and downstream channels for the set of CMs that use orthogonal frequency division multiplexing (OFDM) based on the discovered IGs.

Abstract: A modem circuit associated with a communication system is disclosed. The modem circuit comprises a symbol tracking circuit configured to track a symbol timing associated with a downstream (DS) channel associated with the modem circuit, in accordance with a timing offset estimate. In some embodiments, the timing offset estimate comprises a unified timing offset derived based on one or more external channels associated with the modem circuit that is different from the DS channel. The symbol tracking circuit is further configured to apply a sample rate correction to a DS signal associated with the DS channel, based on the timing offset estimate comprising the unified timing offset, and apply a frequency correction to the DS signal, based on a frequency offset estimate comprising a unified frequency offset derived based on the one or more external channels.

Abstract: A cable modem system for discovering interference groups (IGs) includes an infrastructure and a cable modem termination system (CMTS). The infrastructure is for transferring data. The CMTS is configured to initiate generation of test signals by a set of cable modems (CMs), obtain a set of test measurements for the set of CMs, discover interference groups (IGs) of the set of CMs based on the obtained set of test measurements and assign a plurality of upstream and downstream channels for the set of CMs that use orthogonal frequency division multiplexing (OFDM) based on the discovered IGs.

Abstract: A cable modem supporting full duplex (FDX) operations. The cable modem includes a transmit circuitry configured to process a transmit signal and a receive circuitry configured to process a receive signal. The receive circuitry includes a switchable analog filter configured to filter the receive signal. The switchable analog filter is configurable for different passband frequencies. The receive circuitry also includes a digital compensation filter configured to compensate a difference in frequency response in a specific frequency band due to switching of the switchable analog filter for a different passband frequency. The cable modem also includes an adjacent channel interference (ACI) cancellation filter and an adjacent leakage interference (ALI) cancellation filter. A digital compensation filter is also used in processing the ACI cancellation signal and the ALI cancellation signal to impose or compensate the difference in frequency response due to the switchable analog filter switching.