Substrate processing apparatus, method and system, and method for generating learning data
A substrate processing device and substrate technology, applied in the general control system, control/adjustment system, test/monitoring control system, etc., to achieve the effect of reducing the burden
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Embodiment approach 1
[0095] refer to figure 1 The substrate processing apparatus 100 of this embodiment will be described. figure 1 It is a schematic diagram of the substrate processing apparatus 100 of this embodiment. In detail, figure 1 It is a schematic plan view of the substrate processing apparatus 100 . The substrate processing apparatus 100 is a single-wafer type apparatus that processes the substrates W one by one. In this embodiment, the substrate W is a semiconductor wafer. The substrate W has a substantially disk shape.
[0096] like figure 1 As shown, the substrate processing apparatus 100 includes a plurality of processing units 1, a fluid chamber 100A, a plurality of fluid boxes 100B, a plurality of load ports LP, an indexer robot IR, and a center robot (center robot) CR and control device 101 .
[0097] Each of the load ports LP accommodates a plurality of substrates W in a stacked manner. The loading and unloading robot IR transfers the substrate W between the load port LP...
Embodiment approach 2
[0229] Next, refer to Figure 20 and Figure 21 Embodiment 2 of the present invention will be described. However, the matters different from the first embodiment will be described, and the description of the same matters as those of the first embodiment will be omitted. The second embodiment is different from the first embodiment in that the learning completed model 136 is generated by the learning apparatus 200 .
[0230] Figure 20 It is a figure which shows the board|substrate processing apparatus 100 and the learning apparatus 200 in this embodiment. In this embodiment, the control device 101 of the substrate processing apparatus 100 is as referenced Figure 1 to Figure 13 As described above, the learning data set 135 is generated. The learning data set 135 is input to the learning apparatus 200 .
[0231] The learning apparatus 200 uses the learning data set 135 to generate the learned model 136 . The learned model 136 is input to the control device 101 of the subs...
Embodiment approach 3
[0246] Next, refer to Figure 17 , Figure 18 , Figure 22 and Figure 23 Embodiment 3 of the present invention will be described. However, different matters from Embodiment 1 and Embodiment 2 will be described, and the description of the same matters as Embodiment 1 and Embodiment 2 will be omitted. The third embodiment differs from the first and second embodiments in that the learning device 200 outputs the processing speed information as the target variable from the learned model 136 .
[0247] Figure 22 It is a figure which shows the substrate processing system 300 in this embodiment. like Figure 22 As shown, the substrate processing system 300 includes the substrate processing apparatus 100 and the learning apparatus 200 . The learning apparatus 200 is an example of an information processing apparatus. In the present embodiment, the processing unit 201 of the learning apparatus 200 generates the learned model 136 as described in the second embodiment. The proce...
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