Experts in the Loop: Conditional Variable Selection for Accelerating Post-Silicon Analysis Based on Deep Learning

Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing. Specifically, correct and deep understanding in test cases of manufactured devices is key to enable post-silicon tuning and debugging. This analysis is typically performed by experienced human experts. However, with the fast development in semiconductor industry, test cases can contain hundreds of variables. The resulting high-dimensionality poses enormous challenges to experts. Thereby, some recent prior works have introduced data-driven variable selection algorithms to tackle these problems and achieved notable success. Nevertheless, for these methods, experts are not involved in training and inference phases, which may lead to bias and inaccuracy due to the lack of prior knowledge. Hence, this work for the first time aims to design a novel conditional variable selection approach while keeping experts in the loop. In this way, we expect that our algorithm can be more efficiently and effectively trained to identify the most critical variables under certain expert knowledge. Extensive experiments on both synthetic and real-world datasets from industry have been conducted and shown the effectiveness of our method

Transmitter and Receiver Equalizers Optimization Methodologies for High-Speed Links in Industrial Computer Platforms Post-Silicon Validation

As microprocessor design scales to nanometric technology, traditional post-silicon validation techniques are inappropriate to get a full system functional coverage. Physical complexity and extreme technology process variations introduce design challenges to guarantee performance over process, voltage, and temperature conditions. In addition, there is an increasingly higher number of mixed-signal circuits within microprocessors. Many of them correspond to high-speed input/output (HSIO) links. Improvements in signaling methods, circuits, and process technology have allowed HSIO data rates to scale beyond 10 Gb/s, where undesired effects can create multiple signal integrity problems. With all of these elements, post-silicon validation of HSIO links is tough and time-consuming. One of the major challenges in electrical validation of HSIO links lies in the physical layer (PHY) tuning process, where equalization techniques are used to cancel these undesired effects. Typical current industrial practices for PHY tuning require massive lab measurements, since they are based on exhaustive enumeration methods. In this work, direct and surrogate-based optimization methods, including space mapping, are proposed based on suitable objective functions to efficiently tune the transmitter and receiver equalizers. The proposed methodologies are evaluated by lab measurements on realistic industrial post-silicon validation platforms, confirming dramatic speed up in PHY tuning and substantial performance improvement

Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent Cation Batteries

Data-driven methodology has become a key tool in computationally predicting material properties. Currently, these techniques are priced high due to computational requirements for generating sufficient training data for high-precision machine learning models. In this study, we present a Support Vector Regression (SVR)-based machine learning model to predict the stability of silicon (Si) - alkaline metal alloys, with a strong emphasis on the transferability of the model to new silicon alloys with different electronic configurations and structures. We elaborate on the role of the structural descriptor in imparting transferability to the model that is trained on limited data (~750 Si alloys) derived from the Material Project database. Three popular descriptors, namely X-Ray Diffraction (XRD), Sine Coulomb Matrix (SCM), and Orbital Field Matrix (OFM), are evaluated for representing Si alloys. The material structures are represented by descriptors in the SVR model, coupled with hyperparameter tuning techniques like Grid Search CV and Bayesian Optimization (BO), to find the best performing model for predicting total energy, formation energy and packing fraction of the Si alloy systems. The models are trained on Si alloys with lithium (Li), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and aluminum (Al) metals, where Si-Na and Si-Al systems are used as test structures. Our results show that XRD, an experimentally derived characterization of structures, performs most reliably as a descriptor for total energy prediction of new Si alloys. The study demonstrates that by qualitatively selection of training data, using hyperparameter tuning methods, and employing appropriate structural descriptors, the data requirements for robust and accurate ML models can be reduced.Comment: 23 pages, 7 figure

Reconfigurable reflective arrayed waveguide grating using optimization algorithms

[EN] In this paper we report the experimental realization of a reconfigurable reflective arrayed waveguide grating on silicon nitride technology, using optimization algorithms borrowed from machine learning applications. A dozen of band-shape responses, as well as a spectral resolution change, are demonstrated in the optical telecom C-band, alongside a proof of operation of the same device in the O-band. In the context of programmable and reconfigurable integrated photonics, this building block supports multi-wavelength/band spectral shaping of optical signals that can serve to multiple applications.Ministerio de Economia y Competitividad (Industrial doctorate grant DI-15-08031, PID2019110877GB-I00 BHYSINPICS, TEC2016-80385-P SINXPECT); H2020 Marie Sklodowska-Curie Actions (Training Network MICROCOMB (GA 812818)); Generalitat Valenciana (PROMETEO/2017/103).Fernández, J.; Felip, J.; Gargallo, B.; Doménech, JD.; Pastor Abellán, D.; Domínguez-Horna, C.; Muñoz Muñoz, P. (2020). Reconfigurable reflective arrayed waveguide grating using optimization algorithms. Optics Express. 28(21):31446-31456. https://doi.org/10.1364/OE.404267S3144631456282

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Hardware Trojan Detection Using Machine Learning

The cyber-physical system’s security depends on the software and underlying hardware. In today’s times, securing hardware is difficult because of the globalization of the Integrated circuit’s manufacturing process. The main attack is to insert a “backdoor” that maliciously alters the original circuit’s behaviour. Such a malicious insertion is called a hardware trojan. In this thesis, the Random Forest Model has proposed for hardware trojan detection and this research focuses on improving the detection accuracy of the Random Forest model. The detection technique used the random forest machine learning model, which was trained by using the power traces of the circuit behaviour. The data required for training was obtained from an extensive database by simulating the circuit behaviours with various input vectors. The machine learning model was then compared with the state-of-art models in terms of accuracy in detecting malicious hardware. Our results show that the Random Forest classifier achieves an accuracy of 99.80 percent with a false positive rate (FPR)of 0.009 and a false negative rate (FNR) of 0.038 when the model is created to detect hardware trojans. Furthermore, our research shows that a trained model takes less training time and can be applied to large and complex datasets

Unraveling the Shift to the Entrepreneurial Economy

A major shift in the organization of developed economies has been taking place: away from what has been characterized as the managed economy towards the entrepreneurial economy, or what Kirchhoff (1994) has called dynamic capitalism. In particular, the empirical evidence provides consistent support that (1) the role of entrepreneurship has significantly increased, and (2) a positive relationship exists between entrepreneurial activity and economic performance. However, the factors underlying this observed shift have not been identified in a systematic manner. The purpose of this paper is to suggest some of the factors leading to this shift and implications for public policy. In particular, we find that technological change is a fundamental catalyst underlying the shift from the managed to the entrepreneurial economy. However, it was not just technological change but rather involved a multitude of factors, ranging from the demise of the communist system, increased globalization, new competition for multinational firms and higher levels of prosperity. Recognition of the causes of the shift from the managed to the entrepreneurial economy implies a shift in public policy directions. Rather than to focus of directly and exclusively on promoting new firms and small firms, it may be that the current approach to entrepreneurship policy is misguided. The priority should not be on entrepreneurship policy but rather a more pervasive and encompassing approach, policy consistent with an entrepreneurial economy.