Solving combinatorial optimization problems is essential in scientific, technological, and engineering applications, but can be very time and energy-consuming using classical algorithms executed on digital processors. Oscillator-based Ising machines offer a promising alternative by exploiting the analog coupling between electrical oscillators to solve such optimization problems more efficiently. Here we present the design and the capabilities of our scalable approach to solve Ising and quadratic unconstrained binary optimization problems. This approach includes routable oscillator connections to simplify the time-consuming embedding of the problem into the oscillator network. Our manufactured silicon chip, featuring 1440 oscillators implemented in a 28 nm technology, demonstrates the ability to solve optimization problems in 950 ns while consuming typically 319 μW per node. A frequency, phase, and delay calibration ensures robustness against manufacturing variations. The system is evaluated with multiple sets of benchmark problems to analyze the sensitivity for parameters such as the coupling strength or frequency.

In integrated circuit design compact models are the abstraction layer which connects semiconductor physics and circuit simulation. Established compact models like BSIM provide a powerful platform for many kinds of conventional MOSFETs. However, novel device concepts like reconfigurable FETs (RFETs) come with a higher expressiveness. Due to their altered transport physics as compared to classical inversion mode MOSFETs those devices are hard to describe in a closed form expression by classical compact models. Table models bridge this gap for devices with novel features or materials, but circuit simulation becomes slow and inaccurate due to interpolation and convergence difficulties. Table model data can, however, be translated to closed form expressions, providing equation based models without the need for interpolation during simulation time. This work shows data driven approaches to generate compact models from biasing tables without physical analysis of the device behavior. Two automated modeling techniques are applied to a recently emerged RFET, forming a Verilog-A compact model for DC and transient simulation in Cadence Virtuoso. Drive current is implemented as a neural network, large enough to accurately predict behavior of a multi-gate device. The high dynamic range from $mA$ to $pA$ is covered by combining a linear model for high currents and a logarithmic model for low currents. For transient simulation precise models for electrode charges are essential. Here, symbolic regression provides human-readable closed form expressions which allow direct implementation in Verilog-A. The compact model approach is demonstrated with device data generated from a structural technology model (TCAD). However, the entire modeling flow can directly be used on real device measurements, if a technology model is unavailable or unpractical. We show that the presented machine learning based compact models show better convergence, more accurate predictions and faster simulation $(82$ to 308 times) in Cadence SPECTRE than simple table models generated from the same device.

Standardized fasteners such as bolts are particularly suitable for structurally integrated force measurements, as they are located directly in the force flow and therefore allow the monitoring of the overall system status in-situ via the multi-axis components of the acting forces. Available solutions are either tailored to specific applications or focus solely on measuring individual components such as the axial force. We present a sensor integrated bolt that is capable of measuring axial force and bending torques. Two cavities are designed within the bolt for the measurement and full integration of electronics, allowing for full encapsulation and autonomy of the sensor. Adjustments to the bolt, such as the cavity design, are optimized using a finite element analysis to minimize any weakening of its primary function. Losses in resilience correspond to the reduction of a strength class. The sensor incorporates three bending beams equipped with strain gauges benchmarked through a calibration setup, that demonstrates a low uncertainty of only 2.5% and the capability to decouple axial and bending loads from the sensor’s output signals. The sensor is integrated into a prototype M20 bolt by means of screwing and bonding connection. Measurements confirm a linear force transmission to the sensor with a linearity error of 1.0% for axial loading and a hysteresis error of 1.9 %. Experimental validation confirms the bolt’s capability to detect bending moments under axial pretension, thus allowing for capturing all acting forces on such bolted connections.

In this paper, open loop and closed loop Tunneling Magnetoresistance (TMR) DC Current Transformers (DCCTs) for ion beam diagnostics are presented. The DCCTs employ MR sensors to measure the DC component of the accelerator’s ion beam. A comparative study between Giant Magnetoresistance (GMR) and TMR sensors is presented to illustrate the sensor selection criterion for the DCCT application. The two proposed DCCTs are studied in open and closed loop configurations. A closed loop feedback electronic system is designed to generate a feedback current equivalent to the ion beam current such that the sensor operates at zero flux. Furthermore, theoretical and experimental results for the TMR-based DCCT including noise analysis are presented for both open loop and closed loop configurations. Both configurations’ minimum detectable currents are in the range of microampere. The proposed closed loop hardware prototype has a settling time of less than 15 μs. The measured minimum detectable currents for the open and closed loop TMR-based DCCTs are 128.2 μA/$\sqrt{\mathrm{Hz}}$ and 10.14 μA/$\sqrt{\mathrm{Hz}}$ at 1 Hz, respectively.

Comprehensive data on machines is essential for digitization in the industry. Since standardized machine elements are used in most machines, integrating them with sensors provides the opportunity to acquire data comprehensively from in-situ. The sensor integration must not change the element’s core function and the standardized mechanical interfaces. Hence, sensors must be fully integrated and self-sufficient. Regarding bolts as widely used machine element, a solution that combines all the aforementioned requirements does not yet exist. A main problem in developing sensor integrating machine elements and especially bolts is to overcome the conflicting objectives in defining design space for mechanical and sensory functions. There is a lack of an approach to model the effects of the design space parameters on the mechanical and sensory functions. This paper proposes an optimization function to aid mechanical and electrical engineers in resolving conflicting objectives by balancing function fulfillment when determining design space parameters. Therefore, the effects of the parameters on the mechanical and sensory functions are modelled using FE-analysis and composing an optimization function with weights. This function provides optimal design space parameters. With respect to mountability boundary conditions the optimum for equal weights is at diameters 13.2mm both, increasing v. Mises stress by 29% and strain at sensor position by 80 %. The location of the optimum is very dependent on proper weighting, which resembles a prioritization of the mechanical versus the sensory function fulfillment. This enables engineers to find optimal parameters by balancing the mechanical and the sensory function fulfillment.

oim are specialized in solving combinatorial optimization problems, that can be represented as the Ising model. They exploit the interaction of (integrated) electrical oscillators in a configurable network for the computation. Such systems naturally evolve towards a ground state, which forms a solution to the problem quickly and energy efficiently. This work presents the design of our 400 oscillator node chip in a 28nm technology. The focus is on the analog oscillator and coupler circuits, which determine the computing performance. Weighted optimization problems with up to 6-bit resolution can be solved within just 714ns. A comprehensive experimental analysis based on a versatile benchmark set is provided. We discuss the computation process and investigate the impact of multiple factors including the randomness of the initial oscillator phases, the frequency mismatch, the coupling strength, and the locking strength. A small range of parameters like the coupling strength and locking strength exists, which show the highest accuracy. Extensive benchmarks achieve an accuracy compared to the best-known solution of more than 94.5% for problems with equal weights and 89.8% for weighted problems. This emphasizes, that carefully designed oscillator-based Ising machines (OIMs) are not only fast, but can find solutions near the global optimum.