The technological challenges that must be overcome to realize manufacture below the 22nm node, near the end of the semiconductor roadmap, are daunting. New sources of variation will come into play while previously ignored process mechanisms will become electrically significant enough to require accurate models. Much more sophisticated design tools and methodologies to deal with process variability will be required, while manufacturing process which is oblivious of design intent will be both too conservative and too expensive. The IMPACT+ research team, with its strengths spanning patterning, algorithms, modeling and design automation, plans to address future semiconductor technology challenges via the following two intertwined themes.
We plan to explore future lithography and plasma etch. Notably, IMPACT+ will aim to help establish current and extend future capabilities of EUV lithography. We will develop fundamental understanding of EUV resist limitations and electromagnetic performance of EUV masks. We will also explore reversing the chemistry of Atomic layer Deposition (ALD) to create precise Atomic Layer Etching (ALE) utilizing thermodynamic assessment of plasma-surface interactions followed by experimentation with viable chemistries. Scalable algorithms will be an integral requirement to apply models and optimization methods at the chip-scale. An example IMPACT+ project would be investigation into shot count reduction algorithms by shot overlapping.
- Devices and Design Interface. Patterning and technology choices have tremendous impact on design tools, flows and margins inherent in them. We will explore the complex interactions of manufacturing flows, design adaptivity and design margins. Interactions of future patterning technologies (especially mutli-patterning and EUV) with design will be an IMPACT+ focus. A design-centric evaluation and optimization framework for device and patterning technology will be a second goal. Exploration of future device options, especially gate-all-around MOSFETs is envisioned. The device and patterning research will directly influence models for variability while design tools dictate the abstractions needed. We will develop a Variability Tool Kit to efficiently measure, capture into the hierarchical model structure, and to interface queries on variability data.