| Ref.No: | 92007400 |
| Start date: | 06.12.2023 |
| End date: | 05.12.2024 |
| Approval date: | 06.12.2023 |
| Department: | CHEMICAL ENGINEERING |
| Sector: | PROCESS ANALYSIS AND PLANT DESIGN |
| Financier: | MERCK ELECTRONICS KGaA |
| Budget: | 23.654,00 € |
| Public key: | ΨΧΦΦ46ΨΖΣ4-ΙΚΗ |
| Scientific Responsible: | Assoc. Prof. GEORGIOS KOKKORIS |
| Email: | g.kokkoris@gmail.com |
| Description: | PLASMA ETCHING, EXTENSIVELY USED IN SEMICONDUCTOR MANUFACTURING FOR PATTERN TRANSFER, SUFFERS FROM MACROSCOPIC NON-UNIFORMITY (AT THE WAFER LEVEL) AND BATCH-TO-BATCH SHIFTS DUE TO REACTOR WALL CHANGES, NECESSITATING FREQUENT SEASONING; BOTH MISHITS INCREASE THE PRODUCTION COST. A DOMINANT DEMAND IS ALSO “GREENER” RECIPES, I.E., PROCESSES WITH LOWER GLOBAL WARMING POTENTIAL (GWP), NON-TOXIC GASES, AND EFFICIENT ENERGY USE. WE ENVISION A UNIVERSAL SOLUTION WITH THE POTENTIAL TO TACKLE ALL MODERN CHALLENGES OF PLASMA ETCHING PROCESS: A COMPUTATIONALLY FAST AND ACCURATE DATA-DRIVEN MODEL OF THE PROCESS WILL BE DEVELOPED AND, THROUGH OPTIMIZATION ALGORITHMS, WILL BE UTILIZED TO PROVIDE ANSWERS TO IMPORTANT QUESTIONS REGARDING THE COST, THE UNIFORMITY, THE DRIFTS, THE ENVIRONMENTAL FOOTPRINT, AND THE DESIGN OF NEW RECIPES. THE DEVELOPMENT OF THE MODEL WILL BE BASED ON DATA COMPUTED FROM FIRST-PRINCIPLES MODELS (PHYSICS-BASED MODELS) AND COLLECTED FROM APT EXPERIMENTAL MEASUREMENTS; THESE DATA CAN BE USED TO TRAIN A MACHINE LEARNING SYSTEM TO APPROXIMATE THE OUTPUTS OF THE PHYSICAL PROCESS. ALTHOUGH USING MACHINE LEARNING TO TRAIN A MODEL IS COMPUTATIONALLY EXPENSIVE, ONCE TRAINED, SUCH MODELS ARE ORDERS OF MAGNITUDE FASTER THAN PHYSICS-BASED MODELS. THIS COMPUTATIONAL EFFICIENCY ALLOWS THE QUICK EXPLORATION OF THE COMPLETE PARAMETER SPACE, WHICH IS CRITICAL WHEN PERFORMING OPTIMIZATION STUDIES TO ADDRESS THE PLASMA ETCHING CHALLENGES. |
