Techniques are presented for the application of neural networks to the fabrication of integrated circuits and electronic devices, where example are given for the fabrication of non-volatile memory circuits and the mounting of circuit components on the printed circuit board of a solid state drive (SSD). The techniques include the generation of high precision masks suitable for analyzing electron microscope images of feature of integrated circuits and of handling the training of the neural network when the available training data set is sparse through use of a generative adversary network (GAN).

Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.

Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.

In a production management method of a substrate in a component mounting system having plural component mounting lines configured to include plural component mounting apparatuses, reading production data of an interrupt type substrate; collecting equipment data of each of the component mounting lines; and determining at least one component mounting line to produce the interrupt type substrate based on the read production data of the interrupt type substrate and the collected equipment data of each of the component mounting lines are performed.

Techniques are presented for the application of neural networks to the fabrication of integrated circuits and electronic devices, where example are given for the fabrication of non-volatile memory circuits and the mounting of circuit components on the printed circuit board of a solid state drive (SSD). The techniques include the generation of high precision masks suitable for analyzing electron microscope images of feature of integrated circuits and of handling the training of the neural network when the available training data set is sparse through use of a generative adversary network (GAN).

An orchestration system acquires inspection result data of an inspection object from a plurality of inspection modules on a production line, inputs the acquired inspection result data into a learning model associated with a first inspection module to determine which of a plurality of clusters the inspection object belongs to based on output from the learning model, and acquires a setting value of a parameter used by an inspection module other than the first inspection module, the setting value being stored in a storage unit in accordance with the cluster to which the inspection object belongs.