Publications and Awards
- Rao, P.K.; Bhushan, M.B.; Bukkapatnam, S.T.S.; Zhenyu Kong; Byalal, S.; Beyca, O.F.; Fields, A; Komanduri, R., “Process-Machine Interaction (PMI) Modeling and Monitoring of Chemical Mechanical Planarization (CMP) Process Using Wireless Vibration Sensors“, IEEE Transactions on Semiconductor Manufacturing, vol.27, no.1, pp.1,15, Feb. 2014.
- doi: 10.1109/TSM.2013.2293095
- Abstract (expand for more)We present a deterministic process-machine interaction (PMI) model that can associate different complex time-frequency patterns, including nonlinear dynamic behaviors that manifest in vibration signals measured during a chemical mechanical planarization (CMP) process for polishing blanket copper wafer surfaces to near-optical finish (Ra ~ 5 nm) to specific process mechanisms. The model captures the effects of the nonuniform structural properties of the polishing pad, pad asperities, and machine kinematics on CMP dynamics using a deterministic 2 ° of freedom nonlinear differential equation. The model was validated using a Buehler (Automet 250) bench top CMP machine instrumented with a wireless (XBee IEEE 802.15.4 RF module) multi-sensor unit that includes a MEMS 3-axis accelerometer (Analog Devices ADXL 335). Extensive experiments suggest that the deterministic PMI model can capture such significant signal patterns as aperiodicity, broadband frequency spectra, and other prominent manifestations of process nonlinearity. Remarkably, the deterministic PMI model was able to explain not just the physical sources of various time-frequency patterns observed in the measured vibration signals, but also, their variations with process conditions. The features extracted from experimental vibration data, such as power spectral density over the 115-120 Hz band, and nonlinear recurrence measures were statistically significant estimators (R2 ~ 75%) of process parameter settings. The model together with sparse experimental data was able to estimate process drifts resulting from pad wear with high fidelity (R2 ~ 85%). The signal features identified using the PMI model can lead to effective real-time in-situ monitoring of wear and anomalies in the CMP process.
- URL: View the paper at IEEE
- R. Vairamuthu;M. Brij Bhushan; R. Srikanth; N.Ramesh Babu, “Performance Analysis of Cylindrical Grinding Process with a Portable Diagnostic Tool“, All India Manufacturing Technology, Design and Research Conference -AIMTDR 2014
- Abstract (expand for more)This paper presents an approach to develop a diagnostic tool that can monitor the power drawn by the spindle motor using a power sensor and infeed of grinding wheel using a linear variable differential transformer (LVDT) in cylindrical grinding machine. A combination of spindle power and wheel infeed measurement enables the performance evaluation of grinding process. This evaluation suggests the possibility of optimizing the grinding cycle in order to enhance the efficiency of grinding process. The effectiveness of the developed in-process, portable diagnostic tool is demonstrated with a case study.
- URL: View the paper at the conference website
- Abstract (expand for more)
- Brij Bhushan;”Experimental Investigations of Turbulence”;Indo-German Winter Academy, Pune, India, 2010
- Tutor: Prof. Franz Durst
- Abstract (expand for more)Turbulence is still the unsolved problem of fluid mechanics. Although quite some information has become available through numerous studies of particular turbulent flows, there is still a lack of understanding how turbulence comes about and what causes its properties. Deductions of information from the Navier-Stokes equations are possible but usually result in unknown terms that need to be modeled. Such modeling is tremendously supported by results of detailed experimental studies of turbulent flows. It is pointed out that experiential fluid mechanics has to concentrate on experiments carried out in turbulent flows to yield the information needed to advance turbulence models. In the lecture, various measurement techniques and their application in measuring turbulent flows will be highlighted. Processing of turbulence data is an important lecture which provides an introduction into this technique and explains how it can be used to study mean flow properties, as well as averaged turbulence properties. Particular attention is given to the measurement of the second moments of turbulent fluctuations. Measurements in grid generated turbulence are summarized and it is pointed out that the resultant measurements yield information that present days turbulence models cannot capture. The need for more detailed studies of turbulence is stressed and flows are given that are worthwhile to be investigated experimentally to provide a sound basis for developing improved turbulence models.
- URL: View the presentation at conference website
|1||Banco Foundation Prize||Best academic record (Rank: 1/115) in the Mechanical Engineering (B.Tech.)||2008-2012|
|2||O P Jindal Engineering and Management Scholarship (OPJEMS)||Awarded to 1 student in every batch (1/600) for academic and leadership excellence||2010 & 2011|
|3||University of Tokyo (Todai) – IIT undergraduate students scholarship||Awarded to 8 students out of all the IITs for outstanding performance in academics||2011 & 2012|
|4||Leadership Enrichment and Regional Networking (LEaRN) scholarship||Involved a semester of study (student exchange) at Nanyang Technological University (NTU) Singapore||2011|
|5||Young Engineers' Visitation Program (Lockheed Martin Center for Innovation)||Member of 1 of 5 teams nominated by Indo-US Science and Technology Forum (IUSSTF) for winning the DRDO students robotics competition 2010||2011|