2.7 Variability Challenges in Nanoscale Designs

Silicon photonic interconnect (SPI) is a promising candidate for the communication infrastructure in multiprocessor systems-on-chip (MPSoCs). When employing SPIs with wavelength-division multiplexing (WDM), it is required to precisely match different devices, such as photonic switches, filters, etc, in terms of their central wavelengths. Nevertheless, SPIs are vulnerable to fabrication non-uniformity (a.k.a. process variations), which influences the reliability and performance of such systems. Understanding process variations helps develop system design strategies to compensate for the variations, as well as estimate the implementation cost for such compensations. For the first time, this paper presents a computationally efficient and accurate bottom-up method to systematically study different process variations in passive SPIs. Analytical models to study the impact of silicon thickness and waveguide width variations on strip waveguides and microresonator (MR)-based add-drop filters are developed. Numerical simulations are used to evaluate our proposed method. Furthermore, we designed, fabricated, and tested several identical MRs to demonstrate process variations. The proposed method is applied to a case study of a passive WDM-based photonic switch, which is the building block in passive SPIs, to evaluate its optical signal-to-noise ratio (OSNR) under different variations. The efficiency of our proposed method enables its application to large-scale SPIs in MPSoCs, where employing numerical simulations is not feasible. 12:30 2.7.3 EFFICIENT SPATIAL VARIATION MODELING VIA ROBUST DICTIONARY LEARNING Speaker: Changhai Liao, Fudan University, CN Authors: Changhai Liao1, Jun Tao1, Xuan Zeng1, Yangfeng Su1, Dian Zhou2 and Xin Li3 1Fudan University, CN; 2Fudan University & The University of Texas at Dallas, US; 3Carnegie Mellon University, US Abstract In this paper, we propose a novel spatial variation modeling method based on robust dictionary learning for nanoscale integrated circuits. This method takes advantage of the historical data to efficiently improve the accuracy of wafer-level spatial variation modeling with extremely low measurement cost. Robust regression is adopted by our implementation to reduce the bias posed by outliers. An iterative coordinate descent method is further introduced to solve the dictionary learning problem with consideration of missing data. Our numerical experiments based on industrial measurement data demonstrate that the proposed method achieves up to 70% error reduction over the conventional VP approach without increasing the measurement cost.In this paper, we propose a novel spatial variation modeling method based on robust dictionary learning for nanoscale integrated circuits. This method takes advantage of the historical data to efficiently improve the accuracy of wafer-level spatial variation modeling with extremely low measurement cost. Robust regression is adopted by our implementation to reduce the bias posed by outliers. An iterative coordinate descent method is further introduced to solve the dictionary learning problem with consideration of missing data. Our numerical experiments based on industrial measurement data demonstrate that the proposed method achieves up to 70% error reduction over the conventional VP approach without increasing the measurement cost. 13:00 IP1-9, 239 FAULT TOLERANT NON-VOLATILE SPINTRONIC FLIP-FLOP Speaker: Rajendra Bishnoi, Karlsruhe Institute of Technology (KIT), DE Authors: Rajendra Bishnoi, Fabian Oboril and Mehdi Tahoori, Karlsruhe Institute of Technology (KIT), DE Abstract With technology down scaling, static power has become one of the biggest challenges in a System-On-Chip. Normally-off computing using non-volatile sequential elements is a promising solution to address this challenge. Recently, many non-volatile shadow flip-flop architectures were introduced, in which Magnetic Tunnel Junction (MTJ) cells are employed as backup storing elements. Due to the emerging fabrication processes of magnetic layers, MTJs are more susceptible to manufacturing defects than their CMOS counterparts. Moreover, unlike memory arrays that can effectively be repaired with well-established memory repair and coding schemes, flip-flops scattered in the layout are more difficult to repair. So, without effective defect and fault tolerance for non-volatile flip-flops, the manufacturing yield will be severely affected. Therefore, we propose a Fault Tolerant Non-Volatile Latch (FTNV-L) design, in which we arrange several MTJ cells in such a way that it is resilient to various MTJ faults. Simulation results show that our proposed FTNV-L can effectively tolerate all single MTJ faults with considerably lower overhead than traditional approaches. 13:01 IP110, 291 TOWARDS AUTOMATIC DIAGNOSIS OF MINORITY CARRIERS PROPAGATION PROBLEMS IN HV/HT AUTOMOTIVE SMART POWER ICS Speaker: Yasser Moursy, Sorbonne Universités, UPMC, FR Authors: Yasser Moursy1, Hao Zou1, Ramy Iskander1, Pierre Tisserand2, Dieu-My Ton2, Giuseppe Pasetti3, Ehrenfried Seebacher4, Alexander Steinmair4, Thomas Gneiting5 and