Transport strategy development is critical for the 300 mm wafer fab due to the novel transport facilities, which implies that vehicles can travel not just in one process center but all around the wide fab, and FOUP can be delivered either through stocker or tool-to-tool directly. The two transport issues were studied in this dissertation in order to bring the flexible facility a beneficial result.
The Search Range (SR) assignment is to determine how far the FOUP (or vehicle) from that vehicle (or FOUP) will be considered for transport, and then indirectly limit the DVemp to make the vehicle work effectively. A two-phase approach with simulation is used to develop an appropriate SR. In phase I, the transport records are collected under the number of IV (v) and WF (f) when dispatching occurs, and then the average and standard variation of DVemp under v and f can be summarized for observing the DVemp’s trend and distribution. The DVemp’s trend showed that the number of WF (f) and IV (v) in the system will affect the DVemp when dispatching successfully.
The DVemp’s distribution showed that DVemp gathered between the intervals from -2.5σto 3σof the average distance of DVemp. Further, phase II extends this result to design and evaluate levels of SR. The SR are designed by average DVemp (Vdv,Fd f) plus multiples of standard deviation of DVemp (VSv, FSf ) under different numbers of WF (f) and IV (v) in each system loading: MR, and also to test if SR is required for dispatching (SR0). The simulation results showed that SR affects performance significantly, and the longer SR like SR3 is applicable in a light system such as MR1, and in a heavy system such as MR2and MR3, the shorter SR like SR1.5 is appropriate. That also means ignoring the WFs or IVs far from V or F by assigning an appropriate SR can improve performance.
Furthermore, the demand driven transport strategies in Integrated Dispatching (ID) is researched for the fully-auto manufacturing and the challenge transport mode. Three transport strategies involved in Vehicle Dispatching (VD), namely, the avoid blocking, avoid starvation, and accelerate batch preparation, were developed and implemented in the Tool and Vehicle Dispatching Integrated (TVDI) architecture. Accordingly, there are five levels of decision-making in TVDI, namely, dispatching request, resource checking, candidate selection, dispatching rules, and result execution. Particularly, candidate selection includes FST, FSS, and TSF which belong to TD, and
FSV and VSF which belong to VD. A three-factor full-factorial with 23designs is used to evaluate the transport strategies. The results show that the factors A: blocking, B: starvation, and C: batch preparation significantly affect the performance of WO, CT, and BPT. Interaction analysis, LSD method, and desirability confirm that the combination of A1: avoid blocking, B1: avoid starvation, and C1: accelerate batch preparation (A1B1C1) has the best performance. The results also prove that the function of transport is not only to provide service to production request but also to fully support production like obviating production obstacles and avoiding capacity loss.
Therefore, these topics do not only involve the fully-automated manufacturing characteristic in the 300mm wafer fab, but also further provides the solution for practitioners involved in dispatching software development. For practical implementation, MES could maintain a list of prioritized moves and release the most important move to the AMHS upon request, and the priorities could be continuously updated by MES based on the changes in production status that the authors proposed.
After determining the appropriate SR and ID strategies, the production characteristic focused on production areas where there are particular restrictions might be implemed into TD for further improving the proposed TVDI architecture.
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Appendix A
The Process flow information using in integrated dispatching (ID) study was as follows.
Production functions (area) s(t): step number (process time)
Layer
WS TF ET DF DF* PH IMP CMP CU QC
Sub.
1 1(0.08) 7(5.45) 13(5.60) 3(1.12) 4(25.34) 14(2.83) 1(0.37) 43(40.78) 2 2(1.25) 10(4.80) 1(6.00) 10(2.00) 1(0.54) 1(1.03) 25(15.63)
3 2(1.10) 8(2.15) 5(2.13) 15(5.37)
4 2(1.10) 7(2.05) 5(1.99) 14(5.14)
5 2(1.10) 7(1.77) 5(1.98) 14(4.84)
6 1(0.94) 4(2.2) 2(1.85) 1(5.43) 7(2.05) 6(2.34) 21(14.80)
7 2(1.44) 3(1.20) 1(0.93) 2(11.02) 7(2.31) 15(16.89)
8 1(0.27) 8(5.15) 1(0.93) 13(2.13) 2(0.91) 25(9.38)
9 2(1.10) 7(1.21) 4(2.54) 13(4.85)
10 2(1.10) 4(1.33) 4(2.54) 10(4.97)
11 2(1.10) 7(1.49) 4(2.44) 13(5.03)
12 2(1.10) 4(1.33) 4(2.44) 10(4.87)
13 2(1.10) 7(1.21) 3(1.91) 12(4.22)
14 1(0.27) 8(4.55) 1(0.93) 3(16.33) 11(2.26) 2(1.78) 26(26.10)
15 3(2.10) 7(1.21) 5(4.61) 15(7.91)
16 1(0.50) 3(1.20) 1(0.93) 8(1.54) 4(3.37) 17(7.54)
17 7(4.88) 6(2.45) 12(2.84) 1(0.54) 1(1.03) 27(11.75)
18 11(4.95) 3(2.49) 14(1.67) 1(1.51) 1(0.10) 30(10.72)
19 11(5.00) 3(1.28) 1(4.22) 19(1.95) 4(2.21) 38(14.66)
20 1(0.29) 6(2.91) 10(1.52) 17(4.72)
21 11(5.27) 7(2.60) 1(4.22) 20(2.00) 4(2.21) 43(16.31)
22 1(0.29) 6(2.91) 10(1.52) 17(4.72)
23 11(5.27) 7(2.60) 1(4.22) 20(2.00) 4(2.21) 43(16.31)
24 1(0.29) 6(2.91) 10(1.52) 17(4.72)
25 11(5.00) 7(2.60) 1(4.22) 20(2.00) 4(2.21) 43(16.04)
26 1(0.29) 6(2.91) 10(1.52) 17(4.72)
27 11(5.24) 7(2.60) 1(4.22) 20(2.00) 4(2.21) 43(16.28)
28 1(0.29) 6(2.55) 10(1.52) 17(4.36)
29 12(5.86) 8(3.09) 1(4.22) 17(1.84) 4(2.21) 42(17.22)
30 4(2.16) 8(2.30) 1(4.22) 8(1.21) 21(9.90)
31 5(3.97) 4(1.99) 9(5.96)
32 1(1.07) 9(3.88) 2(1.18) 12(6.12)
33 2(0.57) 2(0.49) 1(5.50) 5(2.87) 2(0.17) 12(9.60)
1 112 170 9 19 339 56 3 25 2 736
Sub. (0.08) (56.81) (80.15) (6.67) (99.17) (59.99) (32.43) (3.57) (13.38) (0.17) (352.41) 0.14 15.22 23.1 1.22 2.58 46.06 7.61 0.41 3.4 0.27 100
% (0.02) (16.12) (22.74) (1.89) (28.14) (17.02) (9.2) (1.01) (3.8) (0.05) (100) s: number of process step of each layer under different production functions;
(t): total net process time (by hour) of each layer under different production functions;
DF: serial step in diffusion process;
DF*: batch step in diffusion process.