High Performance SiGe HBT Performance Variability Learning by Utilizing Neural Networks and Technology Computer Aided Design

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Process sensitivity and variation, such as layer thicknesses, etch dimensions and doping levels are all process parameters that should be well understood when assessing their impact on end of process wafer quality and device performance. Improvements in uniformity of electrical device performance is often not taken into major consideration until a certain maturity level of a technology is reached. In this work, use of technology computer aided design (TCAD) and process compact models (PCM) developed from neural networks demonstrate their utility in process- parameter variation understanding in earlier stages of technology development. A TCAD simulation was built and calibrated to match key electrical performance metrics of an experimental high performance SiGe HBT in 130nm BiCMOS technology, which was the device of focus in this study. Figure 1 includes a TCAD cross section of the device1. Neural net techniques, a machine-learning methodology, the development and deployment of which has grown significantly over the last three decades, is used to expand the scope of TCAD to process variability2-3.The aforementioned calibrated TCAD deck was used to generate training data for the neural network process compact model in place of hardware data. Key process features, as listed in table 1 were a few of the process conditions that were varied from nominal values to characterize the variation in resulting electrical performance. Figure 2 includes a 1D schematic view of the epitaxial layers of the HBT, highlighting some of the process features varied in simulation. The neural network was trained using a data analysis suite using three hidden layers with 16 neurons each to get a best fit to the training data input and output values.The foundation of this approach was a TCAD simulation calibrated to match performance of the nominal HBT device of focus. Key AC and Gummel performance parameters had good agreement with hardware data as illustrated in Figure 3a and b. Thousands of individual TCAD simulations with combinations of varied process feature values were then executed to generate neural network training data in lieu of hardware data- utilizing reported standard deviations of each process parameter. The trends and sensitivity of this data was then reflected in the process compact model, which would provide results for large-scale calculation and analysis. Beta was the electrical parameter of focus in this study, where its changes in process variation (mean and standard deviation) were predicted by the trained PCM. The agreement with hardware electrical parameter Beta with respect to Boron dose in Figure 4 demonstrates how use of calibrated TCAD as a basis can empower prediction of large-scale hardware results without the extent of hardware expense. Use of this tool can be extended to simulate and analyze larger populations than what would be feasible for hardware (in several thousands) to predict the effect of process input changes on uniformity of electrical parameters. Figure 5 demonstrates how the changes in standard deviation of Beta can be predicted as a function of Boron dose variation-through generation of large simulated populations by the PCM.The resulting work on a state-of-the-art SiGe BiCMOS technology demonstrates how specific simulation tools, like TCAD-trained process compact models can be leveraged for enhanced process understanding and improvement. The data generated from these tools can supplement or replace hardware and corresponding data, saving in development costs and associated hours in manpower. They can also enable earlier and more agile decision-making about key production metrics throughout the technology development process. Calibrated TCAD infrastructure, coupled with neural-net technology have potential to fulfill pivotal roles through the timelines of technology development, rather than just being utilized when a technology is mature.