Dean, B., Peyton T., Carpenter, J. L., Sam, D., Peterson, A., Kim, J., Lawrence, S. P., Fadul, M., Reising, D. L., and Loveless, T. D. Machine Learning Approaches for Analysis of Total Ionizing Dose in Microelectronics2013. Retrieved from https://par.nsf.gov/biblio/10426783. 2022 European Conference on Radiation and Its Effects on Components and Systems (RADECS) .

Dean, B., Peyton T., Carpenter, J. L., Sam, D., Peterson, A., Kim, J., Lawrence, S. P., Fadul, M., Reising, D. L., & Loveless, T. D. Machine Learning Approaches for Analysis of Total Ionizing Dose in Microelectronics2013. 2022 European Conference on Radiation and Its Effects on Components and Systems (RADECS), (). Retrieved from https://par.nsf.gov/biblio/10426783.

Dean, B., Peyton T., Carpenter, J. L., Sam, D., Peterson, A., Kim, J., Lawrence, S. P., Fadul, M., Reising, D. L., and Loveless, T. D. "Machine Learning Approaches for Analysis of Total Ionizing Dose in Microelectronics2013". 2022 European Conference on Radiation and Its Effects on Components and Systems (RADECS) (). Country unknown/Code not available. https://par.nsf.gov/biblio/10426783.

@article{osti_10426783,
place = {Country unknown/Code not available}, title = {Machine Learning Approaches for Analysis of Total Ionizing Dose in Microelectronics2013}, url = {https://par.nsf.gov/biblio/10426783}, abstractNote = {}, journal = {2022 European Conference on Radiation and Its Effects on Components and Systems (RADECS)}, author = {Dean, B. and Peyton T. and Carpenter, J. L. and Sam, D. and Peterson, A. and Kim, J. and Lawrence, S. P. and Fadul, M. and Reising, D. L. and Loveless, T. D.}, }