Practical Layout-Aware Analog/Mixed-Signal Design Automation with Bayesian Neural Networks

The high simulation cost has been a bottleneck of practical analog/mixed-signal design automation. Many learning-based algorithms require thousands of simulated data points, which is impractical for expensive to simulate circuits. We propose a learning-based algorithm that can be trained using a small amount of data and, therefore, scalable to tasks with expensive simulations. Our efficient algorithm solves the post-layout performance optimization problem where simulations are known to be expensive. Our comprehensive study also solves the schematic-level sizing problem. For efficient optimization, we utilize Bayesian Neural Networks as a regression model to approximate circuit performance. For layout-aware optimization, we handle the problem as a multi-fidelity optimization problem and improve efficiency by exploiting the correlations from cheaper evaluations. We present three test cases to demonstrate the efficiency of our algorithms. Our tests prove that the proposed approach is more efficient than conventional baselines and state-of-the-art algorithms.Comment: Accepted to the 42nd International Conference on Computer-Aided Design (ICCAD 2023); 8 pages, 8 figure

Do topology and ferromagnetism cooperate at the EuS/Bi2_2Se3_3 interface?

We probe the local magnetic properties of interfaces between the insulating ferromagnet EuS and the topological insulator Bi$_2$Se$_3$ using low energy muon spin rotation (LE-$\mu$SR). We compare these to the interface between EuS and the topologically trivial metal, titanium. Below the magnetic transition of EuS, we detect strong local magnetic fields which extend several nm into the adjacent layer and cause a complete depolarization of the muons. However, in both Bi$_2$Se$_3$ and titanium we measure similar local magnetic fields, implying that their origin is mostly independent of the topological properties of the interface electronic states. In addition, we use resonant soft X-ray angle resolved photoemission spectroscopy (SX-ARPES) to probe the electronic band structure at the interface between EuS and Bi$_2$Se$_3$. By tuning the photon energy to the Eu anti-resonance at the Eu $M_5$ pre-edge we are able to detect the Bi$_2$Se$_3$ conduction band, through a protective Al$_2$O$_3$ capping layer and the EuS layer. Moreover, we observe a signature of an interface-induced modification of the buried Bi$_2$Se$_3$ wave functions and/or the presence of interface states

Metabolic modeling of endosymbiont genome reduction on a temporal scale

This study explores the order in which individual metabolic genes are lost in an in silico evolutionary process leading from the metabolic network of Eschericia coli to that of the genome-reduced endosymbiont Buchnera aphidicola

An Accurate Determination of the Exchange Constant in Sr_2CuO_3 from Recent Theoretical Results

Data from susceptibility measurements on Sr_2CuO_3 are compared with recent theoretical predictions for the magnetic susceptibility of the antiferromagnetic spin-1/2 Heisenberg chain. The experimental data fully confirms the theoretical predictions and in turn we establish that Sr_2CuO_3 behaves almost perfectly like a one-dimensional antiferromagnet with an exchange coupling of J = 1700^{+150}_{-100}K.Comment: revised and reformatted paper with new title to appear in Phys. Rev B (Feb.1996). 3 pages (revtex) with 3 embedded figures (macro included). A complete postscript file is available from http://fy.chalmers.se/~eggert/expsusc.ps or by request from [email protected]

R-symmetry and Supersymmetry Breaking at Finite Temperature

We analyze the spontaneous $U(1)_R$ symmetry breaking at finite temperature for the simple O'Raifeartaigh-type model introduced in [1] in connection with spontaneous supersymmetry breaking. We calculate the finite temperature effective potential (free energy) to one loop order and study the thermal evolution of the model. We find that the R-symmetry breaking occurs through a second order phase transition. Its associated meta-stable supersymmetry breaking vacuum is thermodynamically favored at high temperatures and the model remains trapped in this state by a potential barrier, as the temperature lowers all the way until T=0.Comment: 19 pages, 4 figures - Minor revisions, references added. To appear in JHE

Dark Matter and Pseudo-flat Directions in Weakly Coupled SUSY Breaking Sectors

We consider candidates for dark matter in models of gauge mediated supersymmetry breaking, in which the supersymmetry breaking sector is weakly coupled and calculable. Such models typically contain classically flat directions, that receive one-loop masses of a few TeV. These pseudo-flat directions provide a new mechanism to account for the cold dark matter relic abundance. We discuss also the possibility of heavy gravitino dark matter in such models.Comment: 16 pages, 2 figures. v2: comments, refs adde

Muon Spin Relaxation and Susceptibility Studies of Pure and Doped Spin 1/2 Kagom\'{e}-like system (Cux_xZn1−x_{1-x})3_{3}V2_{2}O7_7(OH)2_{2} 2H2_2O

Muon spin relaxation ($\mu$SR) and magnetic susceptibility measurements have been performed on the pure and diluted spin 1/2 kagom\'{e} system (Cu$_x$Zn$_{1-x}$)$_{3}$V$_{2}$O$_7$(OH)$_{2}$ 2H$_2$O. In the pure $x=1$ system we found a slowing down of Cu spin fluctuations with decreasing temperature towards $T \sim 1$ K, followed by slow and nearly temperature-independent spin fluctuations persisting down to $T$ = 50 mK, indicative of quantum fluctuations. No indication of static spin freezing was detected in either of the pure ($x$=1.0) or diluted samples. The observed magnitude of fluctuating fields indicates that the slow spin fluctuations represent an intrinsic property of kagom\'e network rather than impurity spins.Comment: 4 pges, 4 color figures, Phys. Rev. Lett. in pres

TAG: Learning Circuit Spatial Embedding From Layouts

Analog and mixed-signal (AMS) circuit designs still rely on human design expertise. Machine learning has been assisting circuit design automation by replacing human experience with artificial intelligence. This paper presents TAG, a new paradigm of learning the circuit representation from layouts leveraging text, self-attention and graph. The embedding network model learns spatial information without manual labeling. We introduce text embedding and a self-attention mechanism to AMS circuit learning. Experimental results demonstrate the ability to predict layout distances between instances with industrial FinFET technology benchmarks. The effectiveness of the circuit representation is verified by showing the transferability to three other learning tasks with limited data in the case studies: layout matching prediction, wirelength estimation, and net parasitic capacitance prediction.Comment: Accepted by ICCAD 202