The present invention provides various aspects for processing multiple types of substrates within cleanspace fabricators or for processing multiple or single types of substrates in multiple types of cleanspace environments particularly to form hardware based encryption devices and hardware based encryption equipped communication devices and multi-chip modules such as chiplets. In some embodiments, a collocated composite cleanspace fabricator may be capable of processing semiconductor devices into integrated circuits and then performing assembly operations to result in product in packaged form. Customized smart devices, smart phones and touchscreen devices may be fabricated in examples of a cleanspace fabricator. The assembly processing may include steps to form hardware based encryption.

Embodiments of the present disclosure provide a control method and apparatus for hybrid process recipes, and a device and a medium. The control method includes: acquiring hybrid process recipe operation groups associated with process recipes operated by etching chambers of an etching machine, where different process recipes correspond to different hybrid process recipe operation groups; acquiring a switching rule of different hybrid process recipe operation groups; and controlling, based on a reserved process recipe for a real-time reserved demand of a target etching chamber and a requirement of the switching rule, the etching machine to automatically switch to a target hybrid process recipe operation group associated with the reserved process recipe.

Embodiments disclosed herein comprise a sensor. In an embodiment, the sensor comprises a substrate having a first surface and a second surface opposite from the first surface. In an embodiment, the sensor further comprises a first electrode over the first surface of the substrate, and a second electrode over the first surface of the substrate and adjacent to the first electrode. In an embodiment, the sensor further comprises a barrier layer over the first electrode and the second electrode.

A substrate processing system comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device connected to each of the plurality of process chambers. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

According to one aspect of the technique of the present disclosure, there is provided a method of displaying substrate arrangement data, including: (a) setting each of a transport parameter for determining at least an arrangement of substrates to be loaded into a substrate retainer and carrier information of a carrier storing the substrates to be loaded into the substrate retainer; (b) creating the substrate arrangement data of a case where the substrates are loaded into the substrate retainer based on the transport parameter and the carrier information set in (a); and (c) displaying the substrate arrangement data at least comprising data representing the arrangement of the substrates in a state where the substrates are loaded in the substrate retainer.

A substrate processing system includes a process chamber, one or more robot, a substrate measurement system, and a computing device. The process chamber may process a substrate that will comprise a film and/or feature after the processing. The one or more robot, to move the substrate from the process chamber to a substrate measurement system. The substrate measurement system may measure the film and/or feature on the substrate and generate a profile map of the film and/or feature. The computing device may process data from the profile map using a first trained machine learning model, wherein the first trained machine learning model outputs a first chamber component condition estimation for a first chamber component of the process chamber. The computing device may then determine whether to perform maintenance on the first chamber component of the process chamber based at least in part on the first chamber component condition estimation.

A semiconductor manufacturing process prediction method and a semiconductor manufacturing process prediction device are provided. The semiconductor manufacturing process prediction method includes the following steps. A plurality of process data are obtained. According to the process data, a machine learning model is used to execute prediction and obtain a prediction confidence and a prediction yield. Whether the prediction confidence is lower than a predetermined level is determined. If the prediction confidence is lower than the predetermined level, the machine learning model is modified. According to the process data, the prediction yield is adjusted.

The program code, when executed by a processor, causes the processor to input fabrication data including a plurality of parameters associated with a semiconductor fabricating process to a framework to generate a first class for analyzing the fabrication data, to extract a first parameter targeted for analysis and a second parameter associated with the first parameter from the plurality of parameters and generate a second class for analyzing the first parameter as a sub class of the first class, to modify the first parameter and the second parameter into a data structure having a format appropriate to store in the second class, so as to be stored in the second class, to perform data analysis on the first parameter and the second parameter, to transform the first parameter and the second parameter into corresponding tensor data, and to input the tensor data to the machine learning model.

The present disclosure provides a semiconductor manufacturing process control method and apparatus, a device, and a storage medium. The method includes: analyzing wafer lot information and determining a current product lot of a current product; obtaining historical measurement data within a specified period; when determining that the historical measurement data does not include first measurement data of the current product lot, if determining, based on preset configuration information, that the historical measurement data includes second measurement data of a target product lot, determining a target regulatory data based on the preset configuration information and the second measurement data; and controlling a production parameter of the current product based on the target regulatory data.

A material processing path selection method includes calculating a plurality of candidate material processing paths, determining a bottleneck process tank, and for each of the plurality of candidate material processing paths, calculating a bottleneck process tank utilization rate to select a candidate material processing path with a highest bottleneck process tank utilization rate in the plurality of candidate material processing paths as a target material processing path. The bottleneck process tank is a process tank having a highest use frequency among all process tanks, A use frequency of the process tank is equal to a total process time length of all materials that need to be transferred to the process tank divided by a number of all the materials that need to be transferred to the process tank.