With the substantial surge in the need for high-end masks it becomes increasingly important to raise the capacity of the corresponding production lines. To this end the efficient qualification of matching tools and processes within a production line is of utmost relevance. Matching is typically judged by the processing of dedicated lots on the new tool and process. The amount of qualification lots should on the one hand be very small, as the production of qualification plates is expensive and uses capacity of the production corridor. On the other hand the strict requirements of high-end products induce very tight specification limits on the matching criteria. It is thus often very difficult to assess tool or process matching on the basis of a small amount of lots. In this paper we expound on a machine learning based strategy which assesses the mask characteristics of a qualification plate by learning the typical behavior of these characteristics within the production line variations. We show that by careful selection of reference production plates as well as by setting specification limits based on the production behavior we can manage the qualification tasks efficiently by using a small number of masks. The specification characteristics as well as the specific limits are selected and determined using a Naïve Bayes learner. The resulting performance for prediction of tool and process matching is assessed by considering the resulting receiving operator curve. As a result we obtain an approach towards the assessment of qualification data which enables engineers to assess the tool and process matching using a small amount of matching data under the constraint of substantial measurement uncertainties. As an outlook we discuss how this approach can be used to examine the reverse question of detecting process failures, i.e. the automated ability to raise a flag when the current production characteristics start to deviate from their typical characteristics. Overall, in this paper we show how the rapidly evolving field of machine learning increasingly impacts the semiconductor production process.
Reticle critical dimension uniformity (CDU) is one of the major sources of wafer CD variations which include both
inter-field variations and intra-field variations. Generally, wafer critical dimension (CD) measurement sample size interfield
is much less than intra-field. Intra-field CDU correction requires time-consumption of metrology. In order to
improve wafer intra-field CDU, several methods can be applied such as intra-field dose correction to improve wafer
intra-field CDU. Corrections can be based on CD(SEM) or aerial image metrology data from the reticle. Reticle CDU
and wafer CDU maps are based on scanning electron microscope (SEM) metrology, while reticle inspection intensity
mapping (NuFLare 6000) and wafer level critical dimension (WLCD) utilize aerial images or optical techniques. Reticle
inspecton tools such as those from KLA and NuFlare, offer the ability to collect optical measurement data to produce an
optical CDU map. WLCD of Zeiss has the advantage of using the same illumination condition as the scanner to measure
the aerial images or optical CD.
In this study, the intra-field wafer CDU map correlation between SEMs and aerial images are characterized. The layout
of metrology structures is very important for the correlation between wafer intra-field CDU, measured by SEM, and the
CDU determined by aerial images. The selection of metrology structures effects on the correlation to SEM CD to wafer
is also demonstrated. Both reticle CDU, intensity CDU and WLCD are candidates for intra-field wafer CDU
characterization and the advantages and limitations of each approach are discussed.
Critical dimension uniformity (CDU) is an important parameter for photomask and wafer manufacturing. In
order to reduce long-range CD variation, compensation techniques for mask writers and scanners have been
developed. Both techniques require mask CD measurements with high spatial sampling. Scanning electron
microscopes (SEMs), which provide CD measurements at very high precision, cannot in practice provide the
required spatial sampling due to their low speed. In contrast mask inspection systems, some of which have the
ability to perform optical CD measurements with very high sampling frequencies, are an interesting alternative.
In this paper we evaluate the CDU measurement results with those of a CD-SEM.
Achieving the required critical dimensions (CD) with the best possible uniformity (CDU) on photo-masks has
always played a pivotal role in enabling chip technology. Current control strategies are based on scanning
electron microscopy (SEM) based measurements implying a sparse spatial resolution on the order of ~ 10-2 m
to 10-1 m. A higher spatial resolution could be reached with an adequate measurement sampling, however the
increase in the number of measurements makes this approach in the context of a productive environment
unfeasible. With the advent of more powerful defect inspection tools a significantly higher spatial resolution
of 10-4 m can be achieved by measuring also CD during the regular defect inspection. This method is not
limited to the measurement of specific measurement features thus paving the way to a CD assessment of all
electrically relevant mask patterns. Enabling such a CD measurement gives way to new realms of CD control.
Deterministic short range CD effects which were previously interpreted as noise can be resolved and
addressed by CD compensation methods. This in can lead to substantial improvements of the CD uniformity.
Thus the defect inspection mediated CD control closes a substantial gap in the mask manufacturing process
by allowing the control of short range CD effects which were up till now beyond the reach of regular CD
SEM based control strategies. This increase in spatial resolution also counters the decrease in measurement
precision due to the usage of an optical system.
In this paper we present detailed results on a) the CD data generated during the inspection process, b) the
analytical tools needed for relating this data to CD SEM measurement and c) how the CD inspection process
enables new dimension of CD compensation within the mask manufacturing process. We find that the
inspection based CD measurement generates typically around 500000 measurements with a homogeneous
covering of the active mask area. In comparing the CD inspection results with CD SEM measurement on a
single measurement point base we find that optical limitations of the inspection tool play a substantial role
within the photon based inspection process. Once these shift are characterized and removed a correlation
coefficient of 0.9 between these two CD measurement techniques is found. This finding agrees well with a
signature based matching approach. Based on these findings we set up a dedicated pooling algorithm which
performs on outlier removal for all CD inspections together with a data clustering according to feature
specific tool induced shifts. This way tool induced shift effects can be removed and CD signature
computation is enabled. A statistical model of the CD signatures which relates the mask design parameters on
the relevant length scales to CD effects thus enabling the computation CD compensation maps. The
compensation maps address the CD effects on various distinct length scales and we show that long and short range contributions to the CD variation are decreased. We find that the CD uniformity is improved by 25%
using this novel CD compensation strategy.
Strict reticle critical dimension (CD) control is needed to supply ≤ 20nm wafer technology nodes. In front end
lithographic processes for example, precise temperature control in resist baking steps is considered paramount to limiting
reticle CD error sources. Additionally, current density during writing and focus are continuously tracked in 50kV e-beam
pattern generators (PG) in order to provide stable CD performance. Despite these strict controls (and many others),
feedback compensation strategies are increasingly utilized in mask manufacturing to reach < 2nm 3σ CD uniformity
(CDU). Such compensations require stable reticle CD signatures which can be problematic when alternate or backup
process tools are employed. The AMTC has applied principle component analysis (PCA) to resist CD measurements of
50kV test reticles fabricated with chemically amplified resists (CAR) in order to quantify the resist CDU capabilities of
front and backup lithographic process tools. PCA results elucidate significant resist CDU differences between similar
lithographic process tools that are considered well matched via CDU 3σ comparisons.
The utility of PCA relies on the statistical analysis of large data sets however, reticle CD sampling is typically sparse, on
the 10-2 m or centimeter (cm) scale using conventional scanning electron microscopes (CD SEM). Higher CD spatial
resolutions can be achieved using advanced inspection tools, which provide CD data on a substantially smaller length
scale (10-4 m), thus yielding a considerably larger CD snapshot for front/backup process tool comparisons. Combining
PCA analysis with high spatial resolution CD data provides novel insights into the opportunities for tool and process CD