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Molecular Modeling of Materials Defects for Yield Recovery

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By Ed Korczynski, Sr. Technical Editor

New materials are being integrated into High Volume Manufacturing (HVM) of semiconductor ICs, while old materials are being extended with more stringent specifications. Defects within materials cause yield losses in HVM fabs, and engineers must identify the specific source of an observed defect before corrective steps can be taken. Honeywell Electronic Materials has been using molecular modeling software provided by Scienomics to both develop new materials and to modify old materials. Modeling allowed Honeywell to uncover the origin of subtle solvation-based film defects within Bottom Anti-Reflective Coatings (BARC) which were degrading yield in a customer’s lithographic process module.

Scienomics sponsored a Materials Modeling and Simulations online seminar on February 26th of this year, featuring Dr. Nancy Iwamoto of Honeywell discussing how Scienomics software was used to accelerate response to a customer’s manufacturing yield loss. “This was a product running at a customer line,” explained Iwamoto, “and we needed to find the solution.” The product was a Bottom Anti-Reflective Coating (BARC) organo-silicate polymer delivered in solution form and then spun on wafers to a precise thickness.

Originally observed during optical inspection by fab engineers as 1-2 micron sized vague spots in the BARC, the new defect type was difficult to see yet could be correlated to lithographic yield loss. The defects appeared to be discrete within the film instead of on the top surface, so the source was likely some manner of particle, yet filters did not capture these particles.

The filter captured some particles rich in silicon, as well as other particles rich in carbon. Sequential filtration showed that particles were passing through impossibly small pores, which suggested that the particles were built of deformable gel-like phases. The challenge was to find the material handling or processing situation, which resulted in thermodynamically possible and kinetically probable conditions that could form such gels.

Fig: Materials Processes and Simulations (MAPS) gives researchers access to visualization and analysis tools in a single user interface together with access to multiple simulation engines. (Source: Scienomics)

Molecular modeling and simulation is a powerful technique that can be used for materials design, functional upgrades, process optimization, and manufacturing. The Figure shows a dashboard for Scienomics’ modeling platform. Best practices in molecular modeling to find out-of-control parameters in HVM include a sequential workflow:

  • Build correct models based on experimental observables,
  • Simulate potential molecular structures based on known chemicals and hierarchical models,
  • Analyze manufacturing variabilities to identify excursion sources, and
  • Propose remedy for failure elimination.

Honeywell Electronic Materials researchers had very few experimental observables from which to start:  phenomenon is rare (yet effects yield), not filterable, yet from thermodynamic hydrolysis parameters it must be quasi-stable. Re-testing of product and re-examination of Outgoing Quality Control (OQC) data at the Honeywell production site showed that the molecular weight of the product was consistent with the desired distribution. There was also an observed BARC thickness increase of ~1nm on the wafer associated with the presence of these defects.

Using the modeling platform, Honeywell looked at the solubility parameters for different small molecular chains off of known-branched back-bone centers. Gel-like agglomerations could certainly be formed under the wrong conditions. Once the agglomerations form, they are not very stable so they can probably dis-aggregate when being forced through a filter and then re-aggregate on the other side.

What conditions could induce gel formation? After a few weeks of modeling, it was determined that temperature variations had the greatest influence on the agglomeration, and that variability was strongest at the ~250°K recommended for storage. Storage at 230°K resulted in measurably worse agglomeration, and any extreme in heating/cooling ramp rate tended to reduce solubility.

Molecular modeling was used in a forensic manner to find that the root cause of gel-like defects was related to thermal history:

*   Thermodynamics determined the most likely oligomers that could agglomerate,

*   Temperature-dependent solubility models determined which particles would reach wafers.

Because of the on-wafer BARC thickness increase of ~1nm, fab engineers could use all of the molecular modeling information to trace the temperature variation to bottles installed in the lithographic track tool. The fab was able to change specifications for the storage and handling of the BARC bottles to bring the process back into control.

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