DREAMS: DFM Rule EvAluation using Manufactured Silicon

Abstract

DREAMS (DFM Rule EvAluation using Manufactured Silicon) is a comprehensive methodology for evaluating the yield-preserving capabilities of a set of DFM (design for manufacturability) rules using the results of logic diagnosis performed on failed ICs. DREAMS is an improvement over prior art in that the distribution of rule violations over the diagnosis candidates and the entire design are taken into account along with the nature of the failure (e.g., bridge versus open) to appropriately weight the rules. Silicon and simulation results demonstrate the efficacy of the DREAMS methodology. Specifically, virtual data is used to demonstrate that the DFM rule most responsible for failure can be reliably identified even in light of the ambiguity inherent to a nonideal diagnostic resolution, and a corresponding rule-violation distribution that is counter-intuitive. We also show that the combination of physically-aware diagnosis and the nature of the violated DFM rule can be used together to improve rule evaluation even further. Application of DREAMS to the diagnostic results from an in-production chip provides valuable insight in how specific DFM rules improve yield (or not) for a given design manufactured in particular facility. Finally, we also demonstrate that a significant artifact of DREAMS is a dramatic improvement in diagnostic resolution. This means that in addition to identifying the most ineffective DFM rule(s), validation of that outcome via physical failure analysis of failed chips can be eased due to the corresponding improvement in diagnostic resolution.

Diagnosing Cell Internal Defects Using Analog Simulation-Based Fault Models

“…Recently, many results on diagnosis-driven yield analysis (DDYA) [4], [6], [18] have been published. Various algorithms were proposed to identify the systematic yield limiters from the volume diagnosis results.…”

On improving estimation of root cause distribution of volume diagnosis

“…Volume diagnosis based analysis [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][58][59][60][61][62][63][64][65][66] can be applied to different stages of yield ramp-up and serves different purposes, such as identification and quantification of an existing critical feature [37-42, 47, 48], identification of an unknown systematic feature [43], [51], [62], validation and calibration of DFM rules [43], [63], defect density and distribution estimation for a random defect [64], and yield monitoring [40], [61]. In this work, these approaches are all referred as diagnosis driven yield analysis (DDYA).…”

“…Defect distribution estimation [50]; and 4. Layout shape feature estimation [34], [42], [66], [51][52][53][54][55][56][57][58][59][60].…”

“…With physical defect features reported by diagnosis tools, numerous papers [38], [40], [43], [47], [48], [49], [51], [53], [66], have proposed to use volume (large amount of) diagnosis reports with appropriate statistical analysis to automatically identify a common physical defect feature. Fixing such a common physical defect feature results in improved yield.…”

“…Fixing such a common physical defect feature results in improved yield. Every DDYA work among [38], [40], [43], [47], [48], [49], [51], [53], [66] has had a certain degree of success, as evidenced by their experimental results. In this work, we will focus on investigating RCD, one type of DDYA technique, as discussed in Chapter 3.…”