DTFab: A Digital Twin based Approach for Optimal Reticle Management in Semiconductor Photolithography

Sivasubramanian, Chandrasekhar Komaralingam; Dodge, Robert; Ramani, A.; Bayba, David; Janakiram, Mani; Butcher, Eric A.; Gonzales, Joseph E.; Pedrielli, Giulia

doi:10.1007/s11518-023-5564-x

Cited by 3 publications

(2 citation statements)

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“…Kim et al [20] adopt a deep reinforcement learning approach to learn a near-optimal scheduling policy by incorporating work-in-progress, reticles, and the tardiness of jobs. Sivasubramanian et al [21] propose a simulation-based approach for real-time reticle management problem with the capability of retrieving information from the real-world system and improve upon commonly used policies. In our paper, we consider a static problem, but with additional complications such as the presence of pods and auxiliary-resource travel times.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, constraints ( 15)-( 17) and constraints ( 18)-( 20) ensure a correct sequencing of jobs per reticle and per pod, respectively. Constraint (21) ensures that each a machine can start a job not earlier than time zero. (22) calculates the end time of job on a machine and constraint (23) ensures that a job starts after the immediate preceding job ends.…”

Section: Mixed Integer Programming Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Scheduling a Real-World Photolithography Area With Constraint Programming

Deenen,

Nuijten,

Akcay

2023

IEEE Trans. Semicond. Manufact.

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Section: Literature Reviewmentioning

confidence: 99%