Abstract: A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

Abstract: A method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method including: obtaining yield distribution data including a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, each set including a spatial variation of a process parameter over the substrate or part thereof corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.

Abstract: Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

Abstract: Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

Abstract: A method including obtaining verified values of a characteristic of a plurality of patterns on a substrate produced by a device manufacturing process; obtaining computed values of the characteristic using a non-probabilistic model; obtaining values of a residue of the non-probabilistic model based on the verified values and the computed values; and obtaining an attribute of a distribution of the residue based on the values of the residue. Also disclosed herein are methods of computing a probability of defects on a substrate produced by the device manufacturing process, and of obtaining an attribute of a distribution of the residue of a non-probabilistic model.

Abstract: Methods for training a process model and determining ranking of simulated patterns (e.g., corresponding to hot spots). A method involves obtaining a training data set including: (i) a simulated pattern associated with a mask pattern to be printed on a substrate, (ii) inspection data of a printed pattern imaged on the substrate using the mask pattern, and (iii) measured values of a parameter of the patterning process applied during imaging of the mask pattern on the substrate; and training a machine learning model for the patterning process based on the training data set to predict a difference in a characteristic of the simulated pattern and the printed pattern. The trained machine learning model can be used for determining a ranking of hot spots. In another method a model is trained based on measurement data to predict ranking of the hot spots.

Abstract: A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

Abstract: A method for predicting yield relating to a process of manufacturing semiconductor devices on a substrate, the method including: obtaining a trained first model which translates modeled parameters into a yield parameter, the modeled parameters including: a) a geometrical parameter associated with one or more selected from: a geometric characteristic, dimension or position of a device element manufactured by the process and b) a trained free parameter; obtaining process parameter data including data regarding a process parameter characterizing the process; converting the process parameter data into values of the geometrical parameter; and predicting the yield parameter using the trained first model and the values of the geometrical parameter.

Abstract: A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.

Abstract: A method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method including: obtaining yield distribution data including a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, each set including a spatial variation of a process parameter over the substrate or part thereof corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.

Abstract: A method including obtaining verified values of a characteristic of a plurality of patterns on a substrate produced by a device manufacturing process; obtaining computed values of the characteristic using a non-probabilistic model; obtaining values of a residue of the non-probabilistic model based on the verified values and the computed values; and obtaining an attribute of a distribution of the residue based on the values of the residue. Also disclosed herein are methods of computing a probability of defects on a substrate produced by the device manufacturing process, and of obtaining an attribute of a distribution of the residue of a non-probabilistic model.

Abstract: An apparatus comprising a wireless communication device for enabling the remote control of a medical apparatus The apparatus includes a remote control enable device comprising a transmitter and one or more switches, actuatable by enable buttons. The buttons allow an operator to close the switch(es), causing the transmitter to send a status signal related to the status (open or closed) of the switch(es), to a receiver configured to communicate said status signal to a control unit. The control unit verifies the signal on a number of criteria. When all required criteria have been met, remote control of the medical apparatus is allowed. In an embodiment the remote control enable device is configured to maintain the power supply to a transmitting chip sufficiently long, upon opening a switch, so that a check can be performed on the correct functioning of the switch after the switch has been opened.

Abstract: An apparatus comprising a wireless communication device for enabling the remote control of a medical apparatus. The apparatus includes a remote control enable device comprising a transmitter and one or more switches, actuatable by enable buttons. The buttons allow an operator to close the switch(es), causing the transmitter to send a status signal related to the status (open or closed) of the switch(es), to a receiver configured to communicate said status signal to a control unit. The control unit verifies the signal on a number of criteria. When all required criteria have been met, remote control of the medical apparatus is allowed. In an embodiment the remote control enable device is configured to maintain the power supply to a transmitting chip sufficiently long, upon opening a switch, so that a check can be performed on the correct functioning of the switch after the switch has been opened.

Abstract: The present invention is related to device for selecting one of several triggering apparatuses, which are simultaneously connectable to said device. The triggering apparatuses are arranged for producing each a triggering signal to enable/disable one or more components of a radiation treatment apparatus. The triggering signals depend on detected parameters. The device is configured to receive triggering signals from the several triggering apparatuses, when all of the apparatuses are connected to the device, to receive a selection of one of the triggering apparatuses, to generate a universal triggering signal for the one or more components on the basis of said received triggering signal from the selected triggering apparatus, and to send the universal triggering signal to the one or more components.

Abstract: The present invention is related to a patient positioning imaging device for positioning a patient in a hadron therapy device provided with a rotatable gantry (20). The patient positioning imaging device comprises a rotatable structure (10) provided with an extensible arm or foldable pivoting arm (12) arranged for connecting an imaging beam source (121) and an extensible structure or foldable pivoting structure (14) arranged for carrying an imaging beam receiver (141). The rotatable structure (10) is arranged for taking CBCT shots of the patient while the patient is located in an offset position with respect to an isocentre of the hadron therapy device, said offset position being in the direction of a rotational axis of the rotatable gantry (20). The rotatable structure (10) is arranged for being rotated while the rotatable gantry (20) remains fixed, and while the extensible or pivoting arm (12) and the extensible or pivoting structure (14) are in extended or unfolded position.

Abstract: Therapy system (100) for irradiating a target volume (2) of a patient (1) with a charged particle beam (6), including a beam generator (3), a beam transport system (4), and a nozzle (5) for distributing the beam to the target volume (2), the nozzle (5) being, when in operation, under vacuum. The therapy system comprises an X-ray device (10) which is rotatably mounted inside the nozzle (5) between a first position and a second position. In the first position, an X-Ray source (12) within the X-Ray device is able to emit X-Rays along a charged particle beam path for generating an X-Ray image on a corresponding X-Ray receiving device (11) arranged opposite to the patient (1), said X-Ray image serving to determine a correct position of the target volume (2) with regard to the charged particle beam (6). In the second position, the X-Ray device (10) is set outside of a charged particle beam treatment path envelope (23), so that the charged particle beam (6) can reach and irradiate the target volume (2).

Abstract: The present invention is related to device for selecting one of several triggering apparatuses, which are simultaneously connectable to said device. The triggering apparatuses are arranged for producing each a triggering signal to enable/disable one or more components of a radiation treatment apparatus. The triggering signals depend on detected parameters. The device includes means for receiving triggering signals from the several triggering apparatuses, when all of the apparatuses are connected to the device, input means for selecting one of the triggering apparatuses, means for generating a universal triggering signal for the one or more components on the basis of said received triggering signal from the selected triggering apparatus, and means for sending the universal triggering signal to the one or more components.

Abstract: Therapy system (100) for irradiating a target volume (2) of a patient (1) with a charged particle beam (6), including a beam generator (3), a beam transport system (4), and a nozzle (5) for distributing the beam to the target volume (2), the nozzle (5) being, when in operation, under vacuum. The therapy system comprises an X-ray device (10) which is rotatably mounted inside the nozzle (5) between a first position and a second position. In the first position, an X-Ray source (12) within the X-Ray device is able to emit X-Rays along a charged particle beam path for generating an X-Ray image on a corresponding X-Ray receiving device (11) arranged opposite to the patient (1), said X-Ray image serving to determine a correct position of the target volume (2) with regard to the charged particle beam (6). In the second position, the X-Ray device (10) is set outside of a charged particle beam treatment path envelope (23), so that the charged particle beam (6) can reach and irradiate the target volume (2).

Abstract: The present invention is related to a patient positioning imaging device for positioning a patient in a hadron therapy device provided with a rotatable gantry (20). The patient positioning imaging device comprises a rotatable structure (10) provided with an extensible arm or foldable pivoting arm (12) arranged for connecting an imaging beam source (121) and an extensible structure or foldable pivoting structure (14) arranged for carrying an imaging beam receiver (141). The rotatable structure (10) is arranged for taking CBCT shots of the patient while the patient is located in an offset position with respect to an isocentre of the hadron therapy device, said offset position being in the direction of a rotational axis of the rotatable gantry (20). The rotatable structure (10) is arranged for being rotated while the rotatable gantry (20) remains fixed, and while the extensible or pivoting arm (12) and the extensible or pivoting structure (14) are in extended or unfolded position.