Patent ID: 12205061

DETAILED DESCRIPTION OF THE INVENTION

The invention will be explained in more detail by presenting two preferred exemplary embodiments which disclose respective ways for a proactive quality control system for materials based on data like historical performance, customer factors, attributes, and material processing, raw material, and intermediate factors and attributes.

FIG.1shows an overview about the necessary method steps in both embodiments. The steps itself are performed divergent in every exemplary embodiment dependent on the different conditions. The semiconductor production system12which performs the method is shown inFIG.2. As previously explained its structure can also differ from embodiment to embodiment. Especially the kind of involved computers can differ greatly, depending on how much of the steps is performed by human users with the help of computers and application software or done automatically by specific computers using for instance AI software.

FIG.2discloses an schematic overview about the involved system components. In this example there is a Material Supplier Site14and a Manufacturer Site13wherein the Material Supplier Site14provides production related data1like data about raw material17and its separated parameters2, quality data from the Material Supplier, other data and so on. This data is collected by the Data Collecting Computer9which performs a Data Integration and provides the data then to the used Data Model5,6like an PLS Model or an artificial neural network. The Manufacturer Site13provides data about its own production process, its own quality data, other data etc. This data is then transferred via a Secure Data Transfer Connector. The communication of the data between the two sites13,14is performed by web services, which are assigned to the sites. Additionally or alternatively also third party web services can be used. This could be e.g. a centrally managed by a Digital Data Platform16.

A preferred first embodiment comprises the use of a CMP test vehicle with copper interconnects in the form of serpentines with varying widths below, for example, nm.FIG.3shows the approach in a schematic process overview, summarized it comprises the use of a CMP test vehicle10with metallic interconnects, CMP on said test vehicle10using the slurry under test, physical and electrical characterization, analysis of the data using the previously established Machine Learning model which is run a software performed on a Analyzing Computer11to evaluate the performance of the slurry. This new understanding is then used to refine or improve the slurry. Said test vehicle10is polished using a commercial CMP system tool, that consists of a rotating platen covered by a polishing pad. The wafer is mounted face down in a carrier that is pressed against the pad with a specified force. The polishing pad is saturated with a slurry that is pumped on to the pad. Polishing of the wafer surface occurs as the wafer is rotated on its own axis and moved on the polishing pad while being forced against the same. In order to detect deviations in the CMP process due to, for example, quality issues, the CMP response curve of the interconnect system can be mapped by using a series of slurries with varying particle sizes and shapes. Also different additives to, for example, modify the resulting pH of the slurry can be used.

After polishing, the defectivity caused by the different slurries is probed by both physical and electrical means. Physical characterization of dishing and erosion can be carried out using scanning probe methods such as AFM, or electron microscopy methods such as SEM, Transmission Electron Microscopy (TEM) or related methods. Electrical characterization of said interconnects of varying widths and locations on the wafer is carried out using a semi-automated electrical tester to measure their resistance.

All the obtained physical and electrical characterization information, CMP process parameters and slurries formulation, including abrasive characteristics, additive nature and concentration, and other available quality controls and certificate of analysis (CoA) of each of the raw components, is stored in a database using a Data Collecting computer9.

A Machine Learning algorithm, for example a Neural Network5,6, performed va a software by the aforementioned Analyzing Computer11is used to train a model able to correlate electrical response to all the above mentioned variables explored during the experiment. This allows to clearly understand the intercorrelations existing between CMP process, slurry characteristics and resulting defectivity3and electrical response4. Such understanding can be used to, for example, establish a process baseline and understand the origin of deviations7due to quality issues in the source raw materials or slurry preparation process. It can alternatively be used to optimize the formulation of slurries aimed at different interconnect metals and/or barrier materials.

Another preferred second embodiment comprises the use of a combinatorial sputtering system able to house two or more targets and to modify the substrate temperature during deposition to promote film densification.FIG.4shows an summary of this approach. It comprises of the use of a combinatorial sputtering system to screen Silicon oxynitride compositions, physical characterization and CMP process. All the data is collected in a unified database and a Neural Network model5,6is used to explain the observed response. It will be shown with further details in the following sentences. The system12allows varying the resulting thin film deposition composition by either varying the ratio of power applied to different targets during deposition, and/or by modifying the sputtering atmosphere by introducing different reactive gases such as oxygen and nitrogen. This allows, for example, to obtain a wide compositional screening of Silicon oxynitride thin films. The composition of these films can be obtained by using conventional characterization techniques such as Rutherford Backscattering Spectroscopy (RBS) or other calibrated techniques. The density and thickness of said films can be obtained using X-ray Reflectometry (XRR). Finally, mechanical properties of the films such as hardness and elastic modulus can be obtained using nanoindentation.

The films with varying compositions are polished using a commercial CMP system tool, that consists of a rotating platen covered by a polishing pad. The wafer is mounted face down in a carrier that is pressed against the pad with a specified force. The polishing pad is saturated with a slurry that is pumped on to the pad. Polishing of the wafer surface occurs as the wafer is rotated on its own axis and moved on the polishing pad while being forced against the same. The removal rate (RR) for the slurry is obtained by measuring the thickness of the film after polishing with ellipsometry.

The deposition parameters of the films with varying composition, the physical characterization information, the CMP process parameters and the slurry formulation is stored in a database using a Data Collecting computer9.

A Machine Learning algorithm, like a Neural Network, which is run a software performed on a Analyzing Computer11is used to train a model able to correlate all the above mentioned parameters explored during the experiment. This model allows to clearly understand and predict in the future the intercorrelations existing between thin film composition, slurry formulation and CMP process parameters and performance. Such understanding can be used to, for example, to establish a process baseline and understand the origin of deviations7due to quality issues in the thin film being processed. It can alternatively be used to optimize the formulation of slurries aimed at, for example, widening their applicability to different thin film compositions.

With these approaches realized as an example in the presented two embodiments the batch automation leads to a significant reduction in finished goods quality variation. This allows transparency on expected finished goods quality based on raw material lot selections and predictive models, leading to a further improvement in the lot selection process and less need to rework or scrap batches.

The continuous data integration additionally allows to monitor the prediction quality and improves the used data models based on the ongoing incoming new information. In a further preferred embodiment the system can also be extended to automate the lot selection in non-edge cases, thus reducing the manual effort of the supply chain planner.

LIST OF REFERENCES

1Available production related data2Separated parameters of raw material data3Defictivity4Electric response5Untrained neural network or PLS model6Trained neural network or PLS models7Detected deviations from the baseline8Improved product quality9Data Collecting computer10Test Vehicle11Analyzing computer12Semiconductor Production system13Manufacturer Site14Material Supplier15Data Analyzer16Digital Data Platform17Raw material data