Design, Modeling and Analysis of Non-classical Field Effect Transistors

Transistor scaling following per Moore\u27s Law slows down its pace when entering into nanometer regime where short channel effects (SCEs), including threshold voltage fluctuation, increased leakage current and mobility degradation, become pronounced in the traditional planar silicon MOSFET. In addition, as the demand of diversified functionalities rises, conventional silicon technologies cannot satisfy all non-digital applications requirements because of restrictions that stem from the fundamental material properties. Therefore, novel device materials and structures are desirable to fuel further evolution of semiconductor technologies. In this dissertation, I have proposed innovative device structures and addressed design considerations of those non-classical field effect transistors for digital, analog/RF and power applications with projected benefits. Considering device process difficulties and the dramatic fabrication cost, application-oriented device design and optimization are performed through device physics analysis and TCAD modeling methodology to develop design guidelines utilizing transistor\u27s improved characteristics toward application-specific circuit performance enhancement. Results support proposed device design methodologies that will allow development of novel transistors capable of overcoming limitation of planar nanoscale MOSFETs. In this work, both silicon and III-V compound devices are designed, optimized and characterized for digital and non-digital applications through calibrated 2-D and 3-D TCAD simulation. For digital functionalities, silicon and InGaAs MOSFETs have been investigated. Optimized 3-D silicon-on-insulator (SOI) and body-on-insulator (BOI) FinFETs are simulated to demonstrate their impact on the performance of volatile memory SRAM module with consideration of self-heating effects. Comprehensive simulation results suggest that the current drivability degradation due to increased device temperature is modest for both devices and corresponding digital circuits. However, SOI FinFET is recommended for the design of low voltage operation digital modules because of its faster AC response and better SCEs management than the BOI structure. The FinFET concept is also applied to the non-volatile memory cell at 22 nm technology node for low voltage operation with suppressed SCEs. In addition to the silicon technology, our TCAD estimation based on upper projections show that the InGaAs FinFET, with superior mobility and improved interface conditions, achieve tremendous drive current boost and aggressively suppressed SCEs and thereby a strong contender for low-power high-performance applications over the silicon counterpart. For non-digital functionalities, multi-fin FETs and GaN HEMT have been studied. Mixed-mode simulations along with developed optimization guidelines establish the realistic application potential of underlap design of silicon multi-Fin FETs for analog/RF operation. The device with underlap design shows compromised current drivability but improve analog intrinsic gain and high frequency performance. To investigate the potential of the novel N-polar GaN material, for the first time, I have provided calibrated TCAD modeling of E-mode N-polar GaN single-channel HEMT. In this work, I have also proposed a novel E-mode dual-channel hybrid MIS-HEMT showing greatly enhanced current carrying capability. The impact of GaN layer scaling has been investigated through extensive TCAD simulations and demonstrated techniques for device optimization

Statistical modelling of nano CMOS transistors with surface potential compact model PSP

The development of a statistical compact model strategy for nano-scale CMOS transistors is presented in this thesis. Statistical variability which arises from the discreteness of charge and granularity of matter plays an important role in scaling of nano CMOS transistors especially in sub 50nm technology nodes. In order to achieve reasonable performance and yield in contemporary CMOS designs, the statistical variability that affects the circuit/system performance and yield must be accurately represented by the industry standard compact models. As a starting point, predictive 3D simulation of an ensemble of 1000 microscopically different 35nm gate length transistors is carried out to characterize the impact of statistical variability on the device characteristics. PSP, an advanced surface potential compact model that is selected as the next generation industry standard compact model, is targeted in this study. There are two challenges in development of a statistical compact model strategy. The first challenge is related to the selection of a small subset of statistical compact model parameters from the large number of compact model parameters. We propose a strategy to select 7 parameters from PSP to capture the impact of statistical variability on current-voltage characteristics. These 7 parameters are used in statistical parameter extraction with an average RMS error of less than 2.5% crossing the whole operation region of the simulated transistors. Moreover, the accuracy of statistical compact model extraction strategy in reproducing the MOSFET electrical figures of merit is studied in detail. The results of the statistical compact model extraction are used for statistical circuit simulation of a CMOS inverter under different input-output conditions and different number of statistical parameters. The second challenge in the development of statistical compact model strategy is associated with statistical generation of parameters preserving the distribution and correlation of the directly extracted parameters. By using advanced statistical methods such as principal component analysis and nonlinear power method, the accuracy of parameter generation is evaluated and compared to directly extracted parameter sets. Finally, an extension of the PSP statistical compact model strategy to different channel width/length devices is presented. The statistical trends of parameters and figures of merit versus channel width/length are characterized

Reliability in the face of variability in nanometer embedded memories

In this thesis, we have investigated the impact of parametric variations on the behaviour of one performance-critical processor structure - embedded memories. As variations manifest as a spread in power and performance, as a first step, we propose a novel modeling methodology that helps evaluate the impact of circuit-level optimizations on architecture-level design choices. Choices made at the design-stage ensure conflicting requirements from higher-levels are decoupled. We then complement such design-time optimizations with a runtime mechanism that takes advantage of adaptive body-biasing to lower power whilst improving performance in the presence of variability. Our proposal uses a novel fully-digital variation tracking hardware using embedded DRAM (eDRAM) cells to monitor run-time changes in cache latency and leakage. A special fine-grain body-bias generator uses the measurements to generate an optimal body-bias that is needed to meet the required yield targets. A novel variation-tolerant and soft-error hardened eDRAM cell is also proposed as an alternate candidate for replacing existing SRAM-based designs in latency critical memory structures. In the ultra low-power domain where reliable operation is limited by the minimum voltage of operation (Vddmin), we analyse the impact of failures on cache functional margin and functional yield. Towards this end, we have developed a fully automated tool (INFORMER) capable of estimating memory-wide metrics such as power, performance and yield accurately and rapidly. Using the developed tool, we then evaluate the #effectiveness of a new class of hybrid techniques in improving cache yield through failure prevention and correction. Having a holistic perspective of memory-wide metrics helps us arrive at design-choices optimized simultaneously for multiple metrics needed for maintaining lifetime requirements

STT-MRAM characterization and its test implications

Spin torque transfer (STT)-magnetoresistive random-access memory (MRAM) has come a long way in research to meet the speed and power consumption requirements for future memory applications. The state-of-the-art STT-MRAM bit-cells employ magnetic tunnel junction (MTJ) with perpendicular magnetic anisotropy (PMA). The process repeatabil- ity and yield stability for wafer fabrication are some of the critical issues encountered in STT-MRAM mass production. Some of the yield improvement techniques to combat the e ect of process variations have been previously explored. However, little research has been done on defect oriented testing of STT-MRAM arrays. In this thesis, the author investi- gates the parameter deviation and non-idealities encountered during the development of a novel MTJ stack con guration. The characterization result provides motivation for the development of the design for testability (DFT) scheme that can help test and characterize STT-MRAM bit-cells and the CMOS peripheral circuitry e ciently. The primary factors for wafer yield degradation are the device parameter variation and its non-uniformity across the wafer due to the fabrication process non-idealities. There- fore, e ective in-process testing strategies for exploring and verifying the impact of the parameter variation on the wafer yield will be needed to achieve fabrication process opti- mization. While yield depends on the CMOS process variability, quality of the deposited MTJ lm, and other process non-idealities, test platform can enable parametric optimiza- tion and veri cation using the CMOS-based DFT circuits. In this work, we develop a DFT algorithm and implement a DFT circuit for parametric testing and prequali cation of the critical circuits in the CMOS wafer. The DFT circuit successfully replicates the electrical characteristics of MTJ devices and captures their spatial variation across the wafer with an error of less than 4%. We estimate the yield of the read sensing path by implement- ing the DFT circuit, which can replicate the resistance-area product variation up to 50% from its nominal value. The yield data from the read sensing path at di erent wafer loca- tions are analyzed, and a usable wafer radius has been estimated. Our DFT scheme can provide quantitative feedback based on in-die measurement, enabling fabrication process optimization through iterative estimation and veri cation of the calibrated parameters. Another concern that prevents mass production of STT-MRAM arrays is the defect formation in MTJ devices due to aging. Identifying manufacturing defects in the magnetic tunnel junction (MTJ) device is crucial for the yield and reliability of spin-torque-transfer (STT) magnetic random-access memory (MRAM) arrays. Several of the MTJ defects result in parametric deviations of the device that deteriorate over time. We extend our work on the DFT scheme by monitoring the electrical parameter deviations occurring due to the defect formation over time. A programmable DFT scheme was implemented for a sub-arrayin 65 nm CMOS technology to evaluate the feasibility of the test scheme. The scheme utilizes the read sense path to compare the bit-cell electrical parameters against known DFT cells characteristics. Built-in-self-test (BIST) methodology is utilized to trigger the onset of the fault once the device parameter crosses a threshold value. We demonstrate the operation and evaluate the accuracy of detection with the proposed scheme. The DFT scheme can be exploited for monitoring aging defects, modeling their behavior and optimization of the fabrication process. DFT scheme could potentially nd numerous applications for parametric characteriza- tion and fault monitoring of STT-MRAM bit-cell arrays during mass production. Some of the applications include a) Fabrication process feedback to improve wafer turnaround time, b) STT-MRAM bit-cell health monitoring, c) Decoupled characterization of the CMOS pe- ripheral circuitry such as read-sensing path and sense ampli er characterization within the STT-MRAM array. Additionally, the DFT scheme has potential applications for detec- tion of fault formation that could be utilized for deploying redundancy schemes, providing a graceful degradation in MTJ-based bit-cell array due to aging of the device, and also providing feedback to improve the fabrication process and yield learning