Min-Chie Jeng

Circuit reliability simulation using TMI2

Using simulation to assess the impacts of various reliability mechanisms to circuit performance has become prevail for advanced technologies due to smaller headroom (=Vdd-Vth) and less design margins. This paper reviews existing circuit aging simulation approaches with focus on TMI. The limitations of aging models are also discussed so that reliability simulations can be executed more correctly with the right expectation. An example circuit under multi-waveform and multi-temperature stress is presented to illustrate reliability simulation flow through TMI.

A smart approach for process variation correlation modeling

Process variation has become a serious concern in nanometer technologies. Designs with competitive margins rely on well-characterized statistical models, which must predict the magnitude and scalability of variability accurately. In this paper, we propose a novel approach in creating the statistical models, which tracks the global variation correlation among logic and SRAM devices, hence more realistic. The simulation result is verified with TSMC N28 technology silicon. Two types of circuits, SRAM Vccmin calibration and a SRAM tracking circuit with logic, are discussed in this paper. Different simulation setups are applied on these two circuits to understand the impact of device correlation for the SRAM performance and design margin setting.

Unifying self-heating and aging simulations with TMI2

In this paper, we discuss how to implement the self heating and aging models with TMI. Various examples about self heating and aging simulations with TMI methodology are shown in this paper. Without trading-off the accuracy, the one with proposed TMI approach for self heating simulations takes much shorter simulation time.

A unified aging model with recovery effect and its impact on circuit design

In this paper we not only discuss how to implement the recovery effect with TMI but also give various examples to show that without considering the recovery effect, the design margin of a system can be either under- or over-estimated. To validate our modeling methodology, the simulation results are benchmarked with measurement data.

A comprehensive solution for BEOL variation characterization and modeling

A comprehensive variation model is critical to achieve both competitive design and manufacturing yield in advanced technologies. Conventionally, as long as FEOL (front end of line) statistical model is appropriate, BEOL (back end of line) variations given by lumping multiple variation sources into few corners is enough to achieve reliable simulation results. However, as BEOL contribution is becoming more important with device scaling, simulation results with conventional corner model may not always produce optimal design margin. We thus propose a more comprehensive solution for BEOL variations characterization and modeling associated with statistical Monte Carlo simulation.