Integrated model-based retargeting and optical proximity correction

Abstract

Conventional resolution enhancement techniques (RET) are becoming increasingly inadequate at addressing the challenges of subwavelength lithography. In particular, features show high sensitivity to process variation in low-k1 lithography. Process variation aware RETs such as process-window OPC are becoming increasingly important to guarantee high lithographic yield, but such techniques suffer from high runtime impact. An alternative to PWOPC is to perform retargeting, which is a rule-assisted modification of target layout shapes to improve their process window. However, rule-based retargeting is not a scalable technique since rules cannot cover the entire search space of two-dimensional shape configurations, especially with technology scaling. In this paper, we propose to integrate the processes of retargeting and optical proximity correction (OPC). We utilize the normalized image log slope (NILS) metric, which is available at no extra computational cost during OPC. We use NILS to guide dynamic target modification between iterations of OPC. We utilize the NILS tagging capabilities of Calibre TCL scripting to identify fragments with low NILS. We then perform NILS binning to assign different magnitude of retargeting to different NILS bins. NILS is determined both for width, to identify regions of pinching, and space, to locate regions of potential bridging. We develop an integrated flow for 1x metal lines (M1) which exhibits lesser lithographic hotspots compared to a flow with just OPC and no retargeting. We also observe cases where hotspots that existed in the rule-based retargeting flow are fixed using our methodology. We finally also demonstrate that such a retargeting methodology does not significantly alter design properties by electrically simulating a latch layout before and after retargeting. We observe less than 1% impact on latch Clk-Q and D-Q delays post-retargeting, which makes this methodology an attractive one for use in improving shape process windows without perturbing designed values.

AIR (Aerial Image Retargeting): A novel technique for in-fab automatic model-based retargeting-for-yield

“…Accordingly, we can conclude that for such map the designs with aerial image signature closest to (0.3,0.14) need a large positive bias (retargeting) to counter the effect of the bad initial design. One important observation from figure (10) is that the contours follow a monotonic behavior, which indicates that the AI signature can solely identify the PW weak areas. Moreover, using the aerial image contours curvature and the aerial image slope would add extra degrees of freedom in describing the PW more accurately and allows better separation between the both width and space weak designs.…”

Study of the pattern aware OPC

“…The normalize image log slope (NILS) is a general metric used to predict the image fidelity [3]. The patterns with small NILS are easy to pinch or bridge.…”