Automatic control in microelectronics manufacturing: Practices, challenges, and possibilities

Edgar, Thomas F.; Butler, Stephanie Watts; Campbell, W.J.; Pfeiffer, Carlos F.; Bode, Christopher; Hwang, Sung Bo; Balakrishnan, K.; Hahn, Juergen

doi:10.1016/s0005-1098(00)00084-4

Cited by 184 publications

(77 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective is to reject process disturbance and maximize the overall equipment effectiveness (OEE) [1].…”

Section: Integrated Product and Process Controlmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Product and Process Control for Sustainable Semiconductor Manufacturing

Liang

Huang

2011

Chinese Journal of Chemical Engineering

View full text Add to dashboard Cite

“…The objective is to reject process disturbance and maximize the overall equipment effectiveness (OEE) [1].…”

Section: Integrated Product and Process Controlmentioning

confidence: 99%

“…As a result, great control challenges [1] are emerging due to a shrinking feature size (0.32 m or smaller) and an enlarging wafer diameter (300 mm or bigger). The quest of advanced control strategies becomes more important for sustainable semiconductor manufacturing [2].…”

Section: Introductionmentioning

confidence: 99%

Integrated Product and Process Control for Sustainable Semiconductor Manufacturing

Liang

Huang

2011

Chinese Journal of Chemical Engineering

View full text Add to dashboard Cite

“…APC is considered to include fault detection, fault classification, fault prognosis, and process control, using information about the material to be processed, measured data, and the desired results [9]. APC includes lot-to-lot, wafer-to-wafer, and within-wafer real-time control and can improve performance, yield, and throughput [10].…”

Section: Introductionmentioning

confidence: 99%

Real-time virtual metrology and control for plasma etch

Lynn¹,

MacGearailt

Ringwood³

2012

Journal of Process Control

View full text Add to dashboard Cite

a b s t r a c tPlasma etch is a semiconductor manufacturing process during which material is removed from the surface of semiconducting wafers, typically made of silicon, using gases in plasma form. A host of chemical and electrical complexities make the etch process notoriously difficult to model and troublesome to control. This work demonstrates the use of a real-time model predictive control scheme to control plasma electron density and plasma etch rate in the presence of disturbances to the ground path of the chamber. Virtual metrology (VM) models, using plasma impedance measurements, are used to estimate the plasma electron density and plasma etch rate in real time for control, eliminating the requirement for invasive measurements. The virtual metrology and control schemes exhibit fast set-point tracking and disturbance rejection capabilities. Etch rate can be controlled to within 1% of the desired value. Such control represents a significant improvement over open-loop operation of etch tools, where variances in etch rate of up to 5% can be observed during production processes due to disturbances in tool state and material properties.

show abstract

“…Control is currently used in fabricating small electronic devices for computing [3], but as device size shrinks toward the size of individual atoms, continuum models of the processing dynamics are no longer valid [4,5]. Plant models instead require consideration of discrete atoms and their interactions, using molecular dynamics, Monte Carlo simulations, or quantum mechanics [6].…”

Section: Introductionmentioning

confidence: 99%

Reduction and identification methods for Markovian control systems, with application to thin film deposition

Gallivan

Murray

2003

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

SUMMARYDynamic models of nanometer-scale phenomena often require an explicit consideration of interactions among a large number of atoms or molecules. The corresponding mathematical representation may thus be high dimensional, nonlinear, and stochastic, incompatible with tools in nonlinear control theory that are designed for low-dimensional deterministic equations. We consider here a general class of probabilistic systems that are linear in the state, but whose input enters as a function multiplying the state vector. Model reduction is accomplished by grouping probabilities that evolve together, and truncating states that are unlikely to be accessed. An error bound for this reduction is also derived. A system identification approach that exploits the inherent linearity is then developed, which generates all coefficients in either a full or reduced model. These concepts are then extended to extremely high-dimensional systems, in which kinetic Monte Carlo (KMC) simulations provide the input-output data. This work was motivated by our interest in thin film deposition. We demonstrate the approaches developed in the paper on a KMC simulation of surface evolution during film growth, and use the reduced model to compute optimal temperature profiles that minimize surface roughness.

show abstract

Automatic control in microelectronics manufacturing: Practices, challenges, and possibilities

Cited by 184 publications

References 82 publications

Integrated Product and Process Control for Sustainable Semiconductor Manufacturing

Integrated Product and Process Control for Sustainable Semiconductor Manufacturing

Real-time virtual metrology and control for plasma etch

Reduction and identification methods for Markovian control systems, with application to thin film deposition

Contact Info

Product

Resources

About