Lithography efficiency a cost comparison model. Sven Grünzig

Transcription

1 Lithography efficiency a cost comparison model Sven Grünzig

2 Lithography efficiency a cost comparison model Introduction From the ideal to the real lithography cluster The analysis of the real lithography cluster efficiency Results & Conclusions 2

3 Lithography efficiency a cost comparison model Introduction From the ideal to the real lithography cluster The comparison of the real lithography cluster efficiency Results & Conclusions 3

4 Introduction What kind of model will be shown? Fast cells Slow cells $ e.g. One cluster 3 cells with 200 wph? e.g. One cluster 6 cells with 100 wph Calculation method to compare two lithography cluster (cluster = toolset of litho cells consisting of linked coater, exposure tool, developer) regarding their efficiency (costs, processed wafer) simple model design, first rules of arithmetic Relevant engineering, financial and line operation parameters are included Seven conditions (theses) to adapt the ideal lithography cluster to reality 4

5 Introduction Why was the model developed? 200 mm $$$ $$$ $$$ $$$ 300 mm Wafer value Throughput Equipment Costs starting 1997 Wafer value Throughput Equipment Costs Equipment costs, throughput (as part of equipment costs) and the value of a wafer (by technology and equipment costs) causing much higher costs when equipment stops (just for seconds) in Lithography than somewhere else during semiconductor manufacturing process Question: is there a maximum throughput existing? If yes relevant in reality? No models to analyze from a comprehensive view (engineering and financial) 5

6 Introduction Why is it worth to know about this model? Decision A or B revenue Simple and transparent Top-down analysis done for the toolset and the relevant parameters Bottom-up calculation with engineering and financial parameters New parameter exists in this case for the toolset calculation and enables a more accurate calculation of the OEE (= OEE* ) faster is better, cleanroom costs matter no general rules To be checked for each specific case Costs/area CoO OEE* OEE throughput OEE Overall Equipment Effectiveness CoO Cost of Ownership Bottom up calculation 6

7 Introduction How does a typical single equipment model look like? Decision A or B Costs/area Throughput, OEE and CoO are calculated bottom up from the single CoO equipment to the toolset blurred and not sufficient definition and measurement of throughput (e.g. throughput per lot ) leads to generalized assumptions used for assessments and decisions which might not be valid for all applications Not all parameters visible on a single equipment calculation OEE throughput Bottom up calculation 7

8 Introduction Why is it not only a specific and theoretical approach? Theory is simple and fast to prove But only real data will determine the practical value Data were collected in two 300 mm Fabs to finally justify to publish the model Publication in 2010 at Semiconductor Fabtech ( presentation refers to 8

9 Lithography efficiency a cost comparison model Introduction From the ideal to the real lithography cluster The comparison of the real lithography cluster efficiency Results & Conclusions 9

10 The ideal lithography cluster Seven theses to develop the model Toolset as Black box fast cells slow cells T (th) = T (th) 100% Ideal cluster: f input = f output 2. Theoretical cluster throughput T (th) equal T (th) 3. costs for supply/wafer no impact 7. result: profit $ Lithography = bottleneck 5. New parameter needs to be added 6. Equal distribution/day (product, count) 10

11 The ideal lithography cluster Identified parameter for comparison of two Lithography cluster Ideal cluster Equipment data Product data Business data I Business data II Line Control data Wph (w-t-w, within lot) Product number ROI interval Spare part costs Productivity time Litho Cell number Exp layer number Cluster invest Cleanroom operating costs rework rate Cell space XY wafer value Cleanroom space invest Service contract costs yield Other operating costs 11

12 The real lithography cluster The real lithography cluster shows gaps at the input and output (at its cells) Cycle time : t ctw (sec ) input A 100% B output Ideal cluster: output follows input, No gaps before/after output, lots are not to identify by gaps IDEAL A B No input gaps at A No output gaps at B Product X Product Y Product Z Product X 1 A Real cluster: output not follows input, gaps before/after input/output and during productive time (!): REAL shift lot-to-lot ( inter lot, last wafer of a lot to first wafer of the next lot) or within lot ( intra lot, wafer-to-wafer ) output gaps at B B -> cycle time within lot ( sec) defines wafer per hour (wph) (means: within lot ) 12

13 The real lithography cluster The observed inter lot gaps are linked with operational and/or technical requirements 1 A Inter lot gaps at the input A are not necessarily large enough to create a standby status (!) Are created before the lot arrives the equipment B Product X Product Y Product Z Product X Created or increased inter lot gaps at the output B are created after delivery of the lot, mostly technical requirements (no error breaks), e.g. recipe changes output gaps at B More product (recipe) changes at one cell needed with a low cell count to process the same count of lots and products within the toolset like at the toolset with more but slower cells

14 The real lithography cluster Identified parameter for the comparison of two real Lithography cluster added Real cluster Equipment data Product data Business data I Business data II Line Control data Operating data Wph (w-t-w, within lot) Product number ROI interval Spare part costs Productivity time Litho Wafer/lot Cell number Exp layer number Cluster invest Cleanroom operating costs rework rate Time lot-to-lot with recipe change Cell space XY Wafer value Cleanroom space invest Service contract costs yield Time lot-to-lot w/o recipe change Other operating costs Recipe changes/day 14

15 Lithography efficiency a cost comparison model Introduction From the ideal to the real lithography cluster The comparison of the real lithography cluster efficiency Results & Conclusions 15

16 Comparison of two real lithography cluster Calculate real throughput/year STEP 1 * wafer moves with line control data Calculate theoretical throughput/year* Count of cells Throughput within lot Calculate lost moves by time for recipe and lot changes Calculate count of recipe and lot changes Product data Line control data Operating data Equipment data Bottom up calculation of the cluster throughput: Count of cells and throughput within lot determine the theoretical throughput (adapted to line control data like yield and productivity time) of the wafer moves/year The count for recipe and lot changes is depending on lot size, count of products and different layers The lost moves/year are a result of the count of recipe and lot changes, the lost time by the changes itself and the throughput within lot during the productive time the lost moves within the lost time decrease the theoretical throughput/year 16

17 Comparison of two real lithography cluster calculation STEP 2 Real Throughput/year Toolset A Real Throughput/year Toolset B Calculation of real wafer throughput/year with identical theoretical throughput/year at both toolsets to detect the difference at the impact of the lost moves by the recipe changes at both toolsets: The cell count and the throughput within lot define the theoretical throughput/year of one toolset To compare the efficiency of the toolsets the theoretical throughput/year will be adapted to be equal at both toolsets by changing the count of litho cells and/or by the change of the throughput within lot With the same theoretical throughput both toolsets will have the same start condition to calculate the resulting lost moves needed for the efficiency comparison (to prove the theory of the model) This is not required for a real cost - benefit comparison (e.g. for supplier comparison with fixed wph) 17

18 Comparison of two real lithography cluster Calculate profit STEP 3 Calculate CoO Calculate revenue* Business data I +II Equipment data Product data Real Throughput/ year * with product data Real Throughput/ year Costs fix Count of cells Chip/wafer price Costs variable Space The real throughput - calculated wafer moves ( mask layers ) - is to break down to sellable goods (wafer) The revenue will be calculated with the product data (chip price or wafer value) The CoO is calculated with the business data I + II (which are finally consists of fix and variable costs) and the equipment data (costs for used cleanroom space impact, calculated for invest - depreciation - and operating costs) 18

19 Comparison of two real lithography cluster Comparison STEP 4 Profit Toolset A Profit Toolset B Final comparison with the finally calculated real throughput/year (= wafer out) for each of both toolsets Comparison of both toolsets for revenue ( wafer out * wafer value) and needed effort (costs) vs. wafer value (price) 19

20 Lithography efficiency a cost comparison model Introduction From the ideal to the real lithography cluster The comparison of the real lithography cluster efficiency The comparison of the real lithography cluster efficiency Results & Conclusions 20

21 Results Example 1 Example 2 Recipe Change Time influence Equipment Costs vs. Throughput (within lot) influence Model will be used to compare the fast (cell) toolset A and the slow (cell) toolset B regarding their profit difference/year on the lithography level Toolset A Toolset B wph (cell, within lot)* Cell count 3 6 CoO/year (Mill $) Recipe change time (min) Recipe changes/day (cell)** * wph (intralot, within lot, wafer-to-wafer) = 3600 sec t ctw Basic configuration of toolset A and B cycle time within lot: t ctw (sec) = T wafer n T(wafer 1) n 1, T = Time (sec), n (2.. 25) ** lot queuing up to eight lots, no time delay between two lots (at same recipe) 21

22 Results Example 1 Recipe Change Time impact vs. wafer value comparison of the fast (cell) cluster A and the slow (cell) cluster B regarding their profit difference/year on the lithography level at an identical theoretical cluster throughput of 570 wph (within lot) a) Recipe change time 0.8 min b) Recipe change time 0.4 min Advantage fast cell cluster Advantage slow cell cluster Advantage fast cell cluster Advantage slow cell cluster The count of recipe changes depends on the queuing strategy: here a MIN/MAX range of lots/queue is assumed. The recipe change time is identical at both toolsets within cases a) and within case b). NEW: High throughput is not optimal at any conditions, impact of recipe changes and wafer value exists Lot queuing, operational as well as technical optimization will significantly change the cost-benefit-analysis Advantage slow or fast depends on the wafer value (and the lost moves) and is not direct linked with the CoO 22

23 Results Example 2 Equipment Costs - Throughput ratio vs. wafer value at identical recipe change time Toolset A Toolset B Wph (cell, within lot) 190 (193) 95 (98) Cell count 3 6 CoO/year (Mill $) Recipe change time (min) Recipe changes/day (cell) The basic configuration will be changed ( the throughput within lot), one case (case 1) just at the slow (cell) toolset from 95 to 98 Wph, another case (case 2) only at the fast (cell) toolset from 190 to 193 Wph 23

24 Results Example 2 Equipment Costs - Throughput ratio vs. wafer value at identical recipe change time With basic parameter Case 1 Wph (slow Cell) = 98 Advantage slow cell cluster Case 2 Wph (fast Cell) = 193 Advantage fast cell cluster Throughput within lot (related to the invest) will influence the cost- benefit analysis at most 24

25 Conclusions Influence by key parameter The key parameter throughput within lot, wafer value, invest (Capex) and recipe change time influence each other regarding their final impact on the comparison This finding can just motivate an exact determination of the shown parameters The final decision for the usage of a slow or fast toolset can be made after the calculation, the comparison and the assessment of the likeliest scenarios for all input parameters 25

26 $ (K) Conclusions Validity of the model All analyses were made before 2010 Relevant technical and operational parameters which create the time losses at the recipe changes were and are under an ongoing improvement process by the chip manufacturers and by the vendors therefore: model is still relevant??? The Model proves that the wafer value significantly influences the calculation And the wafer price trend goes up: wafer price trend Source: IBS nm 26

27 Conclusions The treatment of the throughput as a behavior of a single Lithography Cell does not cover the overall toolset influence of this parameter. The relevant parameter is the time spent for recipe changes The count of recipe changes depends on the count of used Lithography Cells The throughput within lot determines the count of needed Lithography Cells The lost time for recipe changes affects the time available to process wafer 27

28 Conclusions The usage of the Overall Equipment Effectiveness is not sufficient enough to use for the definition of the optimal throughput but is required for it. Efficiency = cost benefit analysis, economic efficiency, result vs. effort Effectiveness = percentage of reached target, result vs. target 28

29 Conclusions The Equipment Productivity and OEE analysis of a Lithography Cell has to be adapted to the specific Lithography Cell behavior. The input stream of the Lithography Cell has to be compared with the output stream Operational (input gap) and technical influence (input delay after lot delivery) between lots exists during the productive time (!) - this circumstance can be detected by SEMI E79 and SEMI E10 using SEMI E116 at the input and the output Not all measured gaps and delays (and/or not the whole gap ) at a recipe change are caused by the recipe change itself and are gaps which would occur without a recipe change too The lost time between lots has to be specified and has to be measured apart of the specification and the measurement of the throughput within lot Throughput within lot is solely specified by the cycle time within lot: t ctw (sec) = T wafer n T(wafer 1) n 1, T = Time (sec), n (2.. 25) 29

30 Conclusions The model is applicable for the strategic planning for the ROI phase. The model works with a cost-benefit- analysis for the equipment set The better operative result (EBIT) during the ROI phase can be reached with slow (cell) or fast (cell) toolsets depending on the specific conditions (e.g. wafer value, invest, lost moves) 30

31 Conclusions The model enables the determination of the impact of any single parameter change. The relevant parameter are easily to change (e.g. wafer size, yield, lost time by recipe change, investment costs, throughput) Not only the comparison of different throughput scenarios is possible but also the comparison of the impact of each single parameter (strategic planning during continuous improvement processes) within one toolset for different conditions (e.g. yield, productivity time) Different throughput scenarios, technologies or parameter changes can be compared and assessed for feasibility studies 31

32 Conclusions The determination of the efficient throughput configuration cannot be carried out without knowing and processing all parameters. Ratio of the value of the manufactured sellable goods vs. fixed and variable costs determines the influence of the parameter lost wafer by recipe changes The cost benefit analysis is mainly influenced by the ratio of the equipment costs and the throughput within lot therefore each case is individually to check 32

33 Conclusions The model is applicable for the strategic planning for the ROI phase. Analysis result (preference of a fast or slow cluster configuration) might look different if the CoO analysis is separately carried out and weighted with a higher prioritization than the toolset cost-benefit point of view target of the model is the comparison of the profit (wafer count and value vs. costs, not just moves or costs/area) during the ROI phase If Lithography is no bottleneck the recipe change time does not influence (or influence less) the real throughput of the toolset forecast (utilization) confidence? 33

34 Conclusions Equipment configurations (at one wavelength) with significant different throughput specifications are actually not available as an option during the equipment specification process with the supplier. A reduced throughput decreases the amount of costs for significant cost drivers (e.g. throughput, lens size) of a Lithography Cell (at least at 193 nm and overlying wavelengths) Without the needed throughput/cost matrix the influence cannot be calculated. The model would enable in this case only the comparison of different suppliers or technologies Throughput (wph) equipment costs ($) 75? 100? 125? 150? 175? 200? 34

35 Thank you