|
The current ITRS roadmap details the growing complexity of device design and the latest device-manufacturer's
techniques for tuning their process for each new design generation. In spite of the current desire to incorporate
techniques termed "Design for Manufacture" (DFM) into the sequence, simulations and the design cycle do little more
than optimize feature quality for ideal exposure conditions while testing for shorts, opens and overlay problems over
process variations. Testing in the DFM simulation is performed by the adaptation of a technique unchanged in the last
30 years, the Process Window analysis. With this, mediocre successes seen in chip-design have not taken their share of
the burden of technology advancement. Consequently, process adaptation to each new design has fallen to increasingly
complex setup procedures of the exposure toolsets that customize scanner performance for each new device.
Design optimization by simulation focuses on feature layout optimization for resolution. Design solutions that take
advantage of the full potential spectrum of mask-feature alternatives to increase functional process-space and simplify
setup in manufacturing do not exist since there is no method of feedback. A mechanism is needed that can quantify
design performance robustness, with mask-contributions, to variations in the user's specific manufacturing process.
In this study, a Process Behavior Model methodology is presented for the analysis of feature profiles and films to
derive the relative robustness of response to process variations for alternative OPC designs. Analysis is performed
without regard to the specific mechanics of the design itself. The design alternatives of each OPC feature are shown to
be strong contributors not only to resolution and depth-of-focus but also to the stability of final image response; that is
the ability of the feature profile to remain at optimum under varying conditions of process exposure excursion.
Several different, 70 nm multi-pitch OPC designs are compared for their response stability to fluctuations of the
process. The optimal process corrections on the reticle are shown to be dependent upon not only the final image size at
some optimal exposure point but also on the ability of the design to maintain feature size within tolerance across an
increasingly large process-space of the target production process. The failure of the classic Process Window analysis to
anticipate or provide corrective insight for performance improvement under these conditions is illustrated.
Models are presented that allow the extraction of the nonlinear but systematic interactions of several OPC designs with
the normal fluctuations experienced across the process exposure space plus those introduced by the toolset and filmstack
variation. A method of extracting the systematic component of each feature's design-iteration is derived
providing the ability to quantify the specific OPC response sensitivity to changes in the exposure and process films as
well as drift introduced by the tools of the exposure set.
|
