| AVS 59th Annual International Symposium and Exhibition | |
| Nanomanufacturing Science and Technology Focus Topic | Tuesday Sessions |
| Session NM+MS-TuM |
| Session: | All Invited Session: Challenges of Nanomanufacturing from an Industrial Perspective |
| Presenter: | C. Hunter, Intermolecular, Inc. |
| Authors: | D. Lazovsky, Intermolecular, Inc. C. Hunter, Intermolecular, Inc. |
| Correspondent: | Click to Email |
Nanomanufacturing is inherently more challenging than the production of micron-scale and larger device structures, as interface effects increasingly dominate device performance for nano-scale devices. Theoretical understanding of such effects lags the results of practice, so empirical experimentation is necessary to simultaneously co-optimize multiple critical elements. Such co-optimization using traditional research and development (R&D) methods is typically inefficient, slow and expensive.
Cost-effective nanomanufacturing starts with the development of an optimized device structure, which depends upon our ability to learn about material interactions. For example, while a basic photovoltaic (PV) cell can be made with just 4 layers (n- and p-regions, two contacts), thin-film PV cells designed for optimum efficiency today use additional 10-50nm thick layers to modify band-gaps, optimize light reflection, and extract maximum current.
Even if an optimized nanodevice structure has been identified, it cannot be trivially transferred to high-volume manufacturing (HVM). Different tooling alters process conditions, which generally results in non-optimal final device performance as well as manufacturing yield losses. As an example, while the champion Cu(In,Ga)Se2 (CIGS) cell from a lab has reached >20% conversion efficiency, the best reported from HVM lines today is only ~14%.
Once a nanomanufacturing line is running, experiments are needed to enhance device performance and improve line yield. However, it is inefficient to do R&D using the production line since the experiments must compete with manufacturing runs, and the HVM tools are generally not ideal for experiments. With inefficiency in R&D learning cycles, improving yield is slow and expensive.
A more efficient approach uses a high productivity combinatorial (HPC™) platform—such as that developed by Intermolecular—to dramatically accelerate R&D by 10-100x relative to traditional methods. With unique combinatorial process tools, throughput-matched characterization, and an informatics analysis and data management system, in less than a year we developed a world-class 17.7% active-area efficiency CIGS PV cell using a two-step sulfur-free process flow.
Intermolecular’s HPC platform is purpose-built for the R&D of semiconductor and clean-energy products, and is used in Collaborative Development Programs (CDPs) with a growing number of customers. For example, leading-edge semiconductor memory chips today use dielectrics and metal electrode layers that are only 1-10 nm thick, and HPC technology has accelerated R&D learning-cycles and time-to-market for our customers producing such memory chips.