Ms. Sofia Helpert Profile

Sofia Helpert

at SandBox Semiconductor

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Proceedings Article | 20 March 2019 Presentation + Paper

Predicting and optimizing etch recipes for across the wafer uniformity

Meghali Chopra, Roger Bonnecaze, Yang Ban, Sofia Helpert

Proceedings Volume 10963, 1096309 (2019) https://doi.org/10.1117/12.2515097

KEYWORDS: Semiconducting wafers, Etching, Neural networks, Calibration, Process modeling, Cadmium sulfide, Critical dimension metrology, Plasma, Plasma etching, Data modeling

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Uniformity of critical dimensions (CDs) across a wafer is an increasing challenge as both CDs and tolerances shrink. Plasma etch uniformity is achieved in part through reactor design and in part through the operating conditions or process recipe of the reactor. The identification of a recipe for a specific etch process is time consuming and expensive, requiring extensive experiments and metrology. Here we present two modules in SandBox StudioTM, SB-Bayesian and SBNeuralNet, to accelerate the prediction and optimization of etch recipes for across the wafer uniformity. A model of etch rates across the wafer is created that accounts for injector locations, gas flow rates and distribution and plasma powers. Synthetic experiments on etching line-space patterns on 300 mm wafers are performed and the CDs and their variations are computed at several hundred site locations. SB-Bayesian requires many fewer experiments to be calibrated and achieve an excellent qualitative match with the experimental data. SB-NeuralNet achieves comparable levels of accuracy to SBBayesian at predicting average CDs and uniformity, but it does not perform as well at predicting trends across the wafer. It is shown that neural nets require a prohibitive amount of experimental data to successfully predict wafer patterns. SBBayesian and SB-NeuralNet were used to create detailed process maps across the parameters space of interest to identify optimal recipes to achieve required CDs and tolerances. Both modules can predict optimal recipe conditions for achieving identified target CD and uniformity metrics. Using these tools, etch recipes for across the wafer uniformity are rapidly optimized at lower cost.

Proceedings Article | 20 March 2019 Presentation + Paper

Simulation and optimization of etch on flexible substrates for roll-to-roll processing

Sofia Helpert, Yang Ban, Meghali Chopra, Roger Bonnecaze

Proceedings Volume 10963, 109630T (2019) https://doi.org/10.1117/12.2515104

KEYWORDS: Etching, Calibration, Plasma etching, Amorphous silicon, Plasma, Manufacturing, Reactive ion etching, Analytics, Data modeling

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A methodology is presented to virtually predict etch profiles on flexible substrates across multi-dimensional process spaces using a minimal number of calibration experiments. Simulations and predictions of the physics and chemical kinetics of plasma etch on flexible substrates are performed using the commercial software SandBox StudioTM. The evolution of a trench profile is computed using surface kinetics models and the level set method. Local etch rates include visibility effects to account for partial shielding of the etch as the pattern is developed and the effects of redeposition. The results of the experiments are then used to update the calibrated model parameters. If the process objectives (e.g., sidewall angle, trench critical dimensions, and across the web uniformity) are not achieved, then a new set of experiments is suggested by the methodology. The process is repeated until the optimal process conditions are identified. The methodology is validated by experiments on etching line-space patterns of polysilicon films on polymer substrates. Results with reactive ion etching with either CF₄ and HBr are shown and the optimal etch recipes (power, etch time and gas flow rates) determined. It is found that this coupled simulation-experiment approach is much more efficient than full factorial experimental design at predicting process outcomes. The methodology presented requires 66% fewer experiments reducing the cost of development by a factor of three.

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