With the number of transistors integrated into a single integrated circuit (IC) crossing the one-billion mark and complementary metal-oxide-semiconductor (CMOS) technology scaling pushing device dimensions ever-so-close to atomic scales, variability in transistor performance is becoming the dominant constraint in modern-day CMOS IC design. Developing novel approaches for device characterization, which allow a detailed study of electrical transistor characteristics across large statistical sample sets, is crucial for the proper identification, characterization, and modeling of different physical sources of device variability. On-chip characterization methodologies have the potential to address all of these issues by enabling the characterization of large statistical device sample sets, while also allowing for high measurement quality and throughput.
In this work, a fully-integrated system for on-chip combined capacitance-voltage (C-V) and current-voltage (I-V) characterization of a large integrated test transistor array implemented in a 45-nm bulk CMOS process is presented. On-chip I-V characterization is implemented using a four-point Kelvin measurement technique with 12-bit sub-10 nA current measurement resolution, 10-bit sub-1 mV voltage measurement resolution, and sampling speeds on the order of 100 kHz. C-V characterization is performed using a novel leakage- and parasitics-insensitive charge-based capacitance measurement (CBCM) technique with atto-Farad resolution.
The on-chip system is employed in developing a comprehensive CMOS transistor variability characterization methodology, studying both random and systematic sources of quasi-static device variability. For the first time, combined C-V/I-V characterization of circuit-representative devices is demonstrated and used to extract variations in the under- lying physical parameters of the device. Additionally, the fast current sampling capabilities of the system are used for the characterization of random telegraph noise (RTN) in small area devices. An automated methodology for the extraction of RTN parameters is developed, and the statistics of RTN are studied across device type, bias, and geometry.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D85H8P3D |
| Date | January 2012 |
| Creators | Realov, Simeon Dimitrov |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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