Abstract: A method of training an inference model to determine one or more parameters of a product of a fabrication process from measurements of the product. The method includes obtaining a dataset of measurements of one or more products of the fabrication process, each of the measurements including an array of values obtained by measuring a corresponding one of the products. The method further includes selecting a proper subset of the dataset for use in training the inference model, the subset selected by applying an optimization procedure to an objective function providing a measure of differences between each measurement in the dataset and corresponding reproduced values of the measurements obtained using a reproduction function having a domain including the measurements in the subset and excluding the measurements not in the subset. The method also includes training the inference model using the proper subset of the dataset.

Abstract: A method for ordering and/or selection of latent elements for modeling low dimensional data within a latent space representation, the low dimensional data being a reduced dimensionality representation of input data as determined by a first model component of a model, comprising the steps of training said model and selecting one of said latent element selections based on said training, said training comprising: reducing a dimensionality of the input data to generate said low dimensional data in said latent space representation; training a second model component of said model for each of one or more latent element selections; and optimizing an approximation of the input data as output by said second model component for each said latent element selection, thereby ranking at least one of said plurality of latent elements in the latent space representation based on a contribution of each latent element to the input data.

Abstract: Method for operating a quantum computing element (1) with a network (2) of shuttling lanes (4) having multiple junctions (5) and multiple manipulation zones (7), wherein the method respectively comprises for a plurality of spin qubits (9): a) initializing the qubit (9), b) manipulating the qubit (9) in at least one of the manipulation zones (7), c) reading out the qubit (9), wherein at least one of the qubits (9) is shuttled along the network (2) of the shuttling lanes (4) between steps a) and c) so as to pass at least four different of the junctions (5).

Abstract: Method for designing a quantum computing element (1) for performing a quantum algorithm, wherein the quantum computing element (1) is configured to be operated with a plurality of spin qubits (9) and has a plurality of shuttling lanes (4) with a plurality of building blocks (13), and wherein the method comprises: a) providing a respective mathematical model for each of the building blocks (13), b) providing an initial design of the quantum computing element (1), c) creating a mathematical model of the initial design by combining the mathematical models of the building blocks (13) according to the initial design, d) obtaining an improved design of the quantum computing element (1) using the mathematical model of the initial design created in step c) and taking account of the quantum algorithm to be performed with the quantum computing element (1).

Abstract: Autoencoder models may be used in the field of lithography to estimate, infer or predict a parameter of interest (e.g., metrology metrics). An autoencoder model is trained to predict a parameter by training it with measurement data (e.g., pupil images) of a substrate obtained from a measurement tool (e.g., optical metrology tool). Disclosed are methods and systems for synchronizing two or more autoencoder models for in-device metrology. Synchronizing two autoencoder models may configure the encoders of both autoencoder models to map from different signal spaces (e.g., measurement data obtained from different machines) to the same latent space, and the decoders to map from the same latent space to each autoencoder's respective signal space. Synchronizing may be performed for various purposes, including matching a measurement performance of one tool with another tool, and configuring a model to adapt to measurement process changes (e.g., changes in characteristics of the tool) over time.