United States

The welds of steel structures, used in the bridges and roadway steel decks, are stressed areas where fatigue cracks are prone to occur. Once a fatigue crack occurs in a weld, it spreads and becomes larger, increasing the risk of collapse of the structure. Therefore, it is important to detect fatigue cracks in welds at an early stage. However, the surfaces of the structures are coated to protect the structure itself and the landscape. This makes it difficult to detect cracks using the conventional methods of visual inspection and magnetic particle inspection.&lt;br/&gt;In this study, a sensor probe adapted to the weld shape of the structure was developed for detecting cracks with the surface coating using the unsaturated AC magnetic flux leakage (USAC-MFL). This method utilizes a small AC magnetic field in the unsaturated region of the initial magnetization for steel and measures the magnetic flux leakage due to the crack. We have previously reported that the crack detection of steel plates was realized with low power and high sensitivity using the USAC-MFL [1].&lt;br/&gt;To evaluate the performance of the developed sensor probe, 20 samples of welded joints with an average coating thickness of 125 and 250 μm were prepared. The slits were located in three positions: in the rib, the weld, and the base (Fig. 1). A total of 70 slits, ranging from 1 mm to 5 mm depth, were measured. The differential signals from the two sensors were recorded and analyzed. The same measurements were performed in the non-slit areas to compare the signals of the slits.&lt;br/&gt;Figure 2 shows the result measured at the slits positioned in the weld. The correlation coefficient of slit depth and differential intensity was 0.89, indicating a strong correlation. The same tendency was obtained for the slits in the rib and base. The differential signal intensity of the slit was larger than that of the non-slit area, even 1 mm depth. These results suggest that the USAC-MFL and the developed sensor probe can be used to detect cracks in the welds of steel structures at an early stage with the surface coating.

![](https://assets.underline.io/uploads/markdown_image/1/image/0d1693854f1cfd481032ccf0b4002494.jpg)

Fig. 1 Welded joint sample with surface coating and the sensor probe.

![](https://assets.underline.io/uploads/markdown_image/1/image/0bd665c2474845ff0803bb88336e9692.jpg)

Fig. 2 Dependence of differential signal intensity and slit depth measured in the weld.



2022 Joint MMM-INTERMAG

Crack detection for welded joint with surface coating using unsaturated AC magnetic flux leakage

The aim of ESSCIRC and ESSDERC is to provide an annual European forum for the presentation and discussion of recent advances in solid-state devices and circuits.
The level of integration for system-on-chip design is rapidly increasing.

This is made available by advances in semiconductor technology. Therefore, more than ever before, a deeper interaction among technologists, device experts, IC designers, and system designers is necessary. ESSCIRC and ESSDERC are governed by a common Steering Committee and share Plenary Keynote Presentations and Joint Sessions bridging both device and circuit communities.

Highlights:
* 3 joint keynote presentations
* 3 keynote presentations ESSCIRC
* 3 keynote presentations ESSDERC
* Invited papers with overall coverage of all aspects of advanced devices and circuits
* Presentation of IEEE and ESSCIRC/ESSDERC Awards
* Educational Events

JOINT PLENARY talks live broadcast: September 13, 2021 
ESSCIRC and ESSDERC KEYNOTE talks live broadcast: September 15, 2021 
LIVE Q&A INTERACTIVE sessions for all Conference Technical sessions: September 14 – 16 – 17, 2021 
LIVE Q&A INTERACTIVE sessions for all Educational Events: September 20-21-22, 2021 
(on demand videos available from September 6 until November 30, 2021)

**Please see here for the [ESSCIRC Guide](https://drive.google.com/file/d/10dghzuv4aQBty4QfTIY9Q8hXyqsue8Le/view?usp=sharing), [ESSDERC Guide](https://drive.google.com/file/d/10dx9y2tm9cHPDa4upNUYjfXCf44aPry5/view?usp=sharing), and the [Program at a Glance](https://drive.google.com/file/d/10eU0OLTXEpiNzI5hkqv4mSr43T8cedPP/view?usp=sharing)**

To access ESSCIRC ESSDERC 2021, you must pre-register for the conference.  Instructions on how to access the event will be provided directly to attendees and included on the ESSCIRC ESSDERC conference website. [Register now](https://www.esscirc-essderc2021.org/).

Please register for the ESSCIRC conference to join the event.

ESSCIRC ESSDERC 2021

The aim of ESSCIRC and ESSDERC is to provide an annual European forum for the presentation and discussion of recent advances in solid-state devices and circuits.
The level of integration for system-on-chip design is rapidly increasing.

This is made available by advances in semiconductor technology. Therefore, more than ever before, a deeper interaction among technologists, device experts, IC designers, and system designers is necessary. ESSCIRC and ESSDERC are governed by a common Steering Committee and share Plenary Keynote Presentations and Joint Sessions bridging both device and circuit communities.





The welds of steel structures, used in the bridges and roadway steel decks, are stressed areas where fatigue cracks are prone to occur. Once a fatigue crack occurs in a weld, it spreads and becomes larger, increasing the risk of collapse of the structure. Therefore, it is important to detect fatigue cracks in welds at an early stage. However, the surfaces of the structures are coated to protect the structure itself and the landscape. This makes it difficult to detect cracks using the conventional methods of visual inspection and magnetic particle inspection. In this study, a sensor probe adapted to the weld shape of the structure was developed for detecting cracks with the surface coating using the unsaturated AC magnetic flux leakage (USAC-MFL). This method utilizes a small AC magnetic field in the unsaturated region of the initial magnetization for steel and measures the magnetic flux leakage due to the crack. We have previously reported that the crack detection of steel plates was realized with low power and high sensitivity using the USAC-MFL [1]. To evaluate the performance of the developed sensor probe, 20 samples of welded joints with an average coating thickness of 125 and 250 μm were prepared. The slits were located in three positions: in the rib, the weld, and the base (Fig. 1). A total of 70 slits, ranging from 1 mm to 5 mm depth, were measured. The differential signals from the two sensors were recorded and analyzed. The same measurements were performed in the non-slit areas to compare the signals of the slits. Figure 2 shows the result measured at the slits positioned in the weld. The correlation coefficient of slit depth and differential intensity was 0.89, indicating a strong correlation. The same tendency was obtained for the slits in the rib and base. The differential signal intensity of the slit was larger than that of the non-slit area, even 1 mm depth. These results suggest that the USAC-MFL and the developed sensor probe can be used to detect cracks in the welds of steel structures at an early stage with the surface coating.

![](https://assets.underline.io/uploads/markdown_image/1/image/0d1693854f1cfd481032ccf0b4002494.jpg)

Fig. 1 Welded joint sample with surface coating and the sensor probe.

![](https://assets.underline.io/uploads/markdown_image/1/image/0bd665c2474845ff0803bb88336e9692.jpg)

Fig. 2 Dependence of differential signal intensity and slit depth measured in the weld.

technical paper

The 15th Joint MMM-INTERMAG Conference (2022 Joint) is sponsored jointly by AIP Publishing and the IEEE Magnetics Society. Members of the international scientific and engineering communities interested in recent developments in fundamental and applied magnetism are invited to attend and contribute to the technical sessions. The technical program will include invited and contributed papers in oral and poster sessions, invited symposia, a plenary session, and an evening session, with about 1500 presentations overall. This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

The 2022 Joint Conference will offer both in-person and prerecorded on-demand content.

For tickets to the event, please register at the following link: https://magnetism.org/register

Please register for the Joint MMM-Intermag conference to join the event.

This Conference provides an outstanding opportunity for worldwide participants to meet their colleagues and collaborators and discuss developments in all areas of magnetism research.

We present an implementation of sequential Bayesian experiment design[1] for efficient Ramsey sequence magnetometry using NV centers in diamond. In the Ramsey sequence, the magnetic field is encoded as a phase difference between two spin states, where the phase accumulates over a selected precession time τ, and the experimental signal oscillates as a function of τ with precession frequency ω = γ B. The challenge is to choose values of τ that make efficient use of measurement time tm to reduce the field uncertainty σB. We use the equivalent noise sensitivity η = σB tm1/2 as a figure of merit. Sequential Bayesian experiment design chooses measurement settings (τ) by forecasting measurement outcomes and the effects they would have on the parameter distributions. The measurement benefit is quantified as the change in the information entropy of the parameter distribution and the measurement cost is the time required. With each measurement, more precise parameter distributions are inferred from new measurement data, improving forecasts and τ choices for later iterations. Results plotted in Fig. 1 compare the performance of sequential Bayesian experiment design with three other protocols: a non-adaptive variant of the quantum phase estimation algorithm (QPEA) [2], nonadaptive random selection of τ (Random), and an adaptive heuristic that chooses τ inversely proportional to the frequency uncertainty (Tau) [3]. For consistency, all measurement data is interpreted using Bayesian inference. The main figure plots the evolution of η against combined computation time and simulated measurement time. The vertical axis allows comparison of average uncertainty at a fixed measurement time. The inset plots η2, allowing comparison of time required to achieve a given uncertainty. The SBED protocol is faster than the Tau, Random, and QPEA protocols by factors of 2, 4 and 5, respectively.

![](https://assets.underline.io/uploads/markdown_image/1/image/5112480a93aec4b2523b3d94617abfa6.jpg)

Fig. 1. Comparison of equivalent noise sensitivity η = σB tm1/2 achieved by different measurement protocols. Initial measurements have small effects on the field uncertainty σB as tm increases. After 1 s of measurment, all protocols have converged to aymptotic σB∝ tm-1/2 behavior. The sequential Bayesian experiment design olffers the best performance.

Adaptive Measurements in Quantum Magnetometry

Magnetic nanoparticles (MNPs) have become a relevant technological topic as a result of their broad use in different fields, biomedicine, environmental applications, 3D printing, etc [1], [2], [3]. This wide use, together with the low MNPs concentrations used in most applications, requires the development of low-cost and high sensitivity detectors. So, this work explores the design of a Giant Magnetoimpedance effect (GMI) based detection platform for contactless MNPs detection, enabling its reuse in multiple applications. The GMI effect, based on huge changes in the high-frequency electrical impedance under external magnetic field, has revealed as a useful tool in the design of high sensitivity magnetic sensors [4]. The proposed device is formed by two soft magnetic amorphous wires (Co66Fe12Si13B15Cr4, length 2 cm) placed in parallel and connected electrically in series to increase the effective detection area [5]. Its working principle relies on the GMI response under the effect of the remnant magnetic field generated by Fe3O4 MNPs (diameter 140 nm). The detection target (sample) was prepared by depositing different MNPs masses, m, from 1 to 15 mg, on different glass substrates. The sample to detect was placed over the sensor surface at different distances, d, (0.31 - 13 mm) avoiding physical contact with the sensing wires. Using a voltage divider and a lock-in amplifier (current frequency f = 100 kHz and peak to peak current I = 15 mA.), the changes of the first V1f(H), and second harmonic V2f(H), components of the GMI voltage were studied as a function of m and d. The larger detection capacity (|(Vif(x)-Vif(xmax))/( Vif(xmax))|; i=1,2; x=m,d) under a similar amount of MNPs found for V2f (70%) respecting V1f (10 %), led to the selection of V2f in the final sensor prototype. A homemade electronics for sensor interfacing and signal conditioning was developed to improve both the sensitivity and spatial resolution where MNPs were detectable. The results show how the designed detection platform permits the quantification of low amounts of MNPs at larger distances (order of mm) than other detectors reported in the bibliography under the principles of low-cost, simple design, single-step measurement and reusability.

Non-Linear GMI Based Detection Platform for an Enhanced Contactless and Reusable Detection of Magnetic Nanoparticles

Superconducting quantum interference devices (SQUIDs), which are the most ultrasensitive magnetic sensors, are generally made by microfabrication of superconducting thin films. The bicrystal junction, which is often used in high-temperature superconducting (HTS) Josephson junctions (JJs), has problems expensive bicrystal substrates, and the fact that JJs can only be arranged in a fixed straight line [1-2]. Because the HTS thin film is a type-II superconductor, magnetic fluxes penetrates the HTS thin film and moves for the Lorentz force, which increases the flux noise at low frequencies. In this study, we investigated a method to fabricate low-noise SQUID magnetic sensors by Ga focused ion beam (FIB) irradiation of high-temperature superconducting thin films. When HTS thin film is irradiated with FIB, point defects are introduced into the HTS crystal structure, which leads to changing into normal conduction. The dependence of the critical current change ratio IC/IC0 on the dose was shown in Fig.1. The dependence of the normal resistance RN are also depicted. The thickness of the YBa2Cu3O7-δ (YBCO) film was 50 nm and it was protected by a thin Au layer. IC/IC0 decreased with irradiation fluence and changed into normal conduction at 2.0×1017 ions/cm2; after this point RN increased steeply. HTS nanobridge JJ, were fabricated by FIB irradiation. Figure 2 shows the V-I characteristics of a nanobridge with an HTS film thickness of 50 nm and a bridge width of 120 nm. The critical current was reduced to 264.5 μA and shows a clear Shapiro-steps under microwave irradiation with frequency of 10 GHz. As a method to reduce the magnetic flux noise, we also investigated the introducing of normal conducting pinning sites (anti-dots) into HTS thin films by FIB irradiation. We have confirmed that anti-dots with diameters of several hundred nm can be formed by point irradiation of HTS thin films with FIB. In the future, we will confirm SQUID operation at a nanobridge JJ with an YBCO film thickness of 50 nm and investigate the improvement of SQUID noise characteristics by introducing anti-dots.

![](https://assets.underline.io/uploads/markdown_image/1/image/809e70edfbb475e4d016f9bb15625221.jpg)

Fig. 1. Dependence of IC/IC0 and RN on FIB irradiation fluence.

![](https://assets.underline.io/uploads/markdown_image/1/image/081fb54ef9f65aa79ff804d2d9bcbfa1.jpg)

Fig. 2. V-I characteristics of the HTS nanobridge with a width of 120 nm.

Flux Noise Reduction of HTS SQUID Using Josephson Junctions Made by FIB

The cilium inspired tactile sensor is based on a magnetic sensor located below thin, elongated permanently magnetized elastic pillars. Deformations of the pillar caused by small flows, forces and surface textures change the magnetic field detected by the sensor, thus transducing deformation into an electric signal [1]. In this work, we present a tactile/force sensor based on AlOx tunnel barrier magnetoresistive (TMR) sensors integrated as a monolithic 2D sensitive device [2]. Each sensing direction includes TMR sensors in Wheatstone bridge to minimize noise contamination of the signal [3] and simplifying the signal processing chain. A polydimethylsiloxane matrix with embedded NdFeB particles (at a mass ratio of 65% particles to 35% PDMS) was used to form the cilium on top of the sensor (Fig. 1). The sensor voltage output is connected to a compact electronic interface, allowing it to be easily integrated within another application where sensitive tactile sensing is desired. The fabricated device presents a sensitivity of 3.1 mV/Oe and 3.0 mV/Oe and a bridge offset voltage of 107 mV/V and -13 mV/V in the X and Y sensitive directions, respectively. The characteristic of the cilia sensor, using cilia with and 400 μm diameter by 3 mm height and a magnetic moment of 58 emu cm-3 was compared to its simulated response. A good agreement between results and simulation was achieved, with an root mean square error (RMSE) error of 66 µV for each respective cilium dimension (Fig. 2a). Finally, the cilium was manually actuated in both X and Y directions, with a clear distinction between the actuations of each sensitive direction being observed (Fig. 2b).

![](https://assets.underline.io/uploads/markdown_image/1/image/56b174226d0e921e32fbf18edd4e97e3.jpg)

Fig. 1: Photograph of the assembled cilia sensor.

![](https://assets.underline.io/uploads/markdown_image/1/image/e29e154c204690332435e250463e92a9.jpg)

Fig. 2: a) Measured signal vs tip deformation characteristic and simulated results for a sensor with 3 mm height and 400 μm diameter cilium. b) Measured response for manual actuation of the cilium in both X and Y directions.

2D application-ready magnetoelastic cilia tactile sensing device

The aerospace industry has become increasingly reliant on carbon fibre composite (CFC) as a structural component in aircraft, due to its strength to weight ratio. One of the disadvantages of CFC are their vulnerability to impact damage and external environment changes during flight, which causes barely visible damage to the structure. One solution to this is structural health monitoring (SHM), which monitors the composite structure to provide early detection for damage. Previous work has found that magnetostrictive ribbons [1] and inkjet printed copper coils used as actuator-sensor pairs have good resolution to detect damage to CFC [2]. This research has focused on inkjet printing magnetic sensors, which can be directly attached to the surface of the composite. JetLab IV (by MicroFab) with piezoelectric print head (60um diameter orifice) was used. The sensor designs were printed using Magnetite (20% in DMF, IPA and water) and nickel (2% in NMP and water) nanoparticle dispersion inks, manufactured by Nanoshel LLC on photo paper (25x25mm) (fig 1). A MPMS3-SQUID magnetometer was used to characterise the ink’s magnetisation (fig 2). Magnetite and nickel has saturation magnetisation values of 78 and 27 emu/g respectively. Different designs and printer parameters were explored and their magnetic performance evaluated the magnetic performance of the printed designs using simple mechanical testing such as 3-point bending test and impact testing.

![](https://assets.underline.io/uploads/markdown_image/1/image/93225e2ded45120b9d7b64a6504b9660.jpg)

Fig 1. Magnetite printed design on photo paper

![](https://assets.underline.io/uploads/markdown_image/1/image/b678abbf59eac3de5f65db1d82d9bf3b.jpg)

Fig.2 Hysteresis loop of magnetite and nickel ink

Printing magnetostrictive materials for structural health monitoring of carbon fibre composite

The giant magnetoresistance (GMR) devices are attractive as cost-efficient gradiometers due to a larger magnetoresistance (ΔR/R) in a simple structure [1]. Many studies showed that NiFeCr seed layers promote a strong 111 crystallographic texture of GMR films that gives rise to a large ΔR/R [2-4]. However, the effect of NiFeCr composition on GMR has not been reported. In this work, we made a systematic study on the structure and GMR effect of NixFeyCrz /Fe16Co66Ni18 /[Cu /Fe16Co66Ni18]5 /Ru films deposited on SiOx substrate. NiFeCr and FeCoNi layers were deposited by co-sputtering (Ni, Fe, Cr) and (Co80Fe20, Ni), respectively. Figure 1 shows a contour of ΔR/R for various NixFeyCrz compositions, with a typical ΔR/R vs μ0H curve corresponding to Ni55Cr45 seed layer. The maximum ΔR/R ~ 35±2.5% (sensitivity ~ 0.25 %/mT) is observed for the seed layers (Ni0.55Cr0.45)100-yFey with 0 ≤ y ≤ 47, indicating that Ni:Cr = 55:45 ratio is critical for large ΔR/R. The estimated bilinear (J1) and biquadratic (J2) interlayer exchange coupling in this region show stronger antiparallel coupling (J1/J2 ≥ 1). On the other hand, for Ni:Cr ≥ 62:38 or Ni:Cr ≤ 48:52, ΔR/R reduces to &lt; 22% as antiparallel coupling reduced (J1/J2 &lt; 1). XRD θ-2θ profiles confirm that [FeCoNi/Cu] multilayer has fcc 111 texture with the diffraction peaks (2θ111) located between 43.70 and 43.96°. We found a clear correlation between 2θ111 and ΔR/R; i.e. films with high ΔR/R ~ 35±2.5% (i.e. on seed layers (Ni0.55Cr0.45)100-yFey with 0 ≤ y ≤ 47) show 43.88° ≤ 2θ111 ≤ 43.96°, whereas films with ΔR/R ≤ 25% show 2θ111&lt; 43.82°. We also calculated the sheet resistance of GMR films at parallel magnetization (RP in Ω/■). Figure 2 consists a contour of ΔR/R plotted for corresponding values of 2θ111 and RP, which clearly shows that films with high ΔR/R consists 43.88°≤ 2θ111≤ 43.96° and lower RP. As lower RP associates to larger grain size [5], these observations indicate that the seed layers of (Ni0.55Cr0.45)100-yFey with 0 ≤ y ≤ 47 induce large gain size in the [FeCoNi/Cu] multilayer, thus realizes the large ΔR/R and sensitivity.

![](https://assets.underline.io/uploads/markdown_image/1/image/4346a8190fe3318f3b32ca88c88cdf33.jpg)

Fig.1: Contour of ΔR/R for various NixFeyCrz seed layers, with a typical ΔR/R vs μ0H.

![](https://assets.underline.io/uploads/markdown_image/1/image/86da0d555064a39961ce21673bf3bcf0.jpg)

Fig.2: Contour map of ΔR/R plotted against 2θ111 and RP.

The compositional dependence of NiFeCr seed layer on the giant magnetoresistance of [FeCoNi/Cu] multilayers

Because emerging wide-bandgap devices enable higher frequency switching, modeling of high-frequency magnetic components becomes increasingly important in designing a high-density switching mode power supply. In such difficult situations, the power electronics circuit designers can be helped with obtaining customized and non-linear models for the magnetic components with measurements to reduce their development time and cost [1-5]. Recent studies indicate that the accurate prediction for the model relies on rectangular-voltage-excited measurements [6]. Especially, the double-pulse-test (DPT) is the preferred test method to measure the switching parameters and evaluate the dynamic behaviors of power devices. On the other hand, image classification using deep learning (DL) technology has already been shown to produce results with very high accuracy, in some cases even surpassing human capabilities [7]. Therefore, we propose a large-signal power magnetic components analysis technique combining DPT and DL shown in Fig. 1 for the classification, the non-linear modeling, and the loss estimation. The unique point of the proposed technique is using the measured waveform image data of DPT. The multi-dimension waveform images include the individual characteristics of the measured magnetic components. The project goal is that the power electronics designer can easily have the large-signal characteristics of the magnetic components on their own. In this presentation, the experimental and analysis results applying for the classification of magnetics components with the proposed technique are discussed as our first step for this project. For example, the classification of three different materials and component styles using commonly available inductor whose nominal inductance value is indicated as 100uH. The results are summarized in the table in Fig. 2. And all of the indexes show perfect results, which means the classification is completed. Thus, the results reveal that the large-signal waveforms include very important information for the magnetic characterization, and the image recognition with DL helps the analysis of the waveforms.

![](https://assets.underline.io/uploads/markdown_image/1/image/ad3acfdc6698b51ca477999c19a60f9e.jpg)

Fig. 1 Proposed Analysis System

![](https://assets.underline.io/uploads/markdown_image/1/image/6f0df08734decb06c4beb137cc4cba4d.jpg)

Fig. 2 Evaluated Process and The Results

Large-Signal Characterization Technique for Power Electronics Magnetic Components by Deep Learning with Double Pulse Test Measurement Image

The strong demand for 3D packaging technology has accelerated the miniaturization of passive devices, and inductors are facing challenges because the inductance value will be sacrificed when the size is shrinking. The magnetic core is one of the solutions due to its enhancement of inductance density but it will also add complexity to the fabrication process. Previous research [1-3] shows that the performance enhancement brought by the integration of magnetic will be shadowed by the process complexity. A novel method to fabricate the magnetic core inductors on flexible substrates is proposed in this work. The inductor has a stacking structure of three layers: the Cu trace layer, and the two layers of magnetic core (under and above the Cu). The Cu trace is patterned by the wet etch process on the flexible PCB, and the upper layer magnetic core is directly sputtered on the Cu trace layer with the polyimide layer being planarized on the Cu surface as insulation. A polyimide layer on the carrier wafer is prepared as the substrate for the lower magnetic core layer and the pattern is also created by the liftoff process. Then, the two finished layers are aligned and bonded with a flip-chip bonder with high precision and efficiency. The whole process is shown in Fig.1. Before the whole fabrication process, the device patterns are designed and optimized by the finite element analysis software to extract the parameters for comparison. And finally, the finished devices (shown in Fig.2) are measured with the radiofrequency probe station. The result shows that the inductance density and the quality factor of the magnetic core inductor could be 3.61 nH/mm2 and 6.22, respectively, with the device size being only 0.95 mm2. Compare with the air core inductor with the same Cu trace pattern, the magnetic core inductor elevates the inductance by 10% and still keeps the same low profile, of which the thickness of the whole device is within 100 um. These parameters are measured and extracted at 100 MHz and this is the widely used frequency for the power delivery applications such as the in-chip integrated voltage regulators.

![](https://assets.underline.io/uploads/markdown_image/1/image/9d73279a565e56c01aa0293d4f0c5845.jpg)

The Fabrication process.

![](https://assets.underline.io/uploads/markdown_image/1/image/0fc585fb76a9a86add06825af359c037.jpg)

The finished device with the magnetic core on the top and bottom layers.

A Low-cost Novel Method to Fabricate Integrated Magnetic Core Inductor Embedded in Organic Substrate

Magnetic nanocomposites have applications in power electronics, where properties such as high permeability, high thickness, high operating frequency, and low loss are desired. Recent work shows that electroinfiltration, where a magnetic composite is formed by electroplating a metal through a deposited nanoparticle layer, produces magnetic composites with high permeabilities and low losses [1]. However, electroinfiltration has a thickness limitation of &lt;4 μm. Attempts to overcome this by fabricating multiple composite layers have been demonstrated, but reveal a trade-off between composite thickness and maximum operating frequency [2]. This trade-off is due to dimensional resonance, caused by the formation of standing waves in the material cross-section. Laminations can be used to overcome this effect, as the resulting laminated layers have smaller cross-sections that increase the frequency at which standing waves begin to form [3]. This work demonstrates a process to create thick magnetic composites by laminating layers of infiltrated magnetic composite with insulating layers of parylene to reduce dimensional resonance effects, as seen in Figure 1. For the magnetic layer, composites made from iron-oxide nanoparticles infiltrated with nickel-metal are used. Four samples are made, containing 1, 2, 3, and 5 layers of magnetic composite, separated from each other by approximately 1-2.5 μm of parylene. Structural characterization of these samples was done using SEM with EDS capabilities, showing that the desired laminated structures were achieved. Next, dc magnetic characterization showed that the magnetic properties of the laminated composites remain nearly constant with the increase in thicknesses. Finally, ac magnetic measurements reveal that these laminated composites have a constant dimensional resonance of ~350 MHz irrespective of an increase in the number of layers, as seen in Figure 2. In conclusion, it is evident that laminating the nanocomposites reduces the effects of dimensional resonance, and a fabrication process for implementing laminations into electroinfiltrated composites has been demonstrated.

![](https://assets.underline.io/uploads/markdown_image/1/image/5ea576300cbef4d9bd7faac9c6149a5f.jpg)

![](https://assets.underline.io/uploads/markdown_image/1/image/a047796add6514afab8b783c426ceed4.jpg)

Fabrication of thick, laminated, electroinfiltrated magnetic nanocomposites to reduce dimensional resonance effects

Fabrication of thick, laminated, electroinfiltrated magnetic nanocomposites to reduce dimensional resonance effects

Power electronics is attracting strong attention as one of the key technologies for realizing the carbon neutral society. A higher switching frequency in a power module is sought for making it more compact. However, inductors hinder it because the loss in the magnetic core rapidly increases. Thus, it is crucial to study and eventually suppress the magnetic loss in MF, HF and VHF bands (300 kHz – 300 MHz). Conventional RF permeameters measure mutual or self inductance of the coil wound around the magnetic sample. This method has the following problems, 1) it requires the sample to be the final toroidal shape, and 2) it cannot apply an external magnetic field. It is desirable to measure the RF permeability of a single particle in the powder stage as a function of magnetic field to gain more insight. For this purpose, we developed the transformer coupled permeameter (TC-Perm), and successfully measured a single Sendust particle used in noise suppression sheet over 10 MHz – 20 GHz1). Then we modified it to further expand the bandwidth. In the initial configuration of TC-Perm, the lower bandwidth limit is set by the vector network analyzer (VNA). We replaced it with another VNA covering 100 kHz – 44 GHz. The S21 parameter of the jig is proportional to frequency at low frequencies because the two CPWs are inductively coupled. This causes the signal level becoming smaller than the noise floor level of the VNA below a few 10 MHz, making it impossible to measure at low frequencies. We modified the TC-Perm as shown in Fig. 1 to avoid this problem. A low noise amplifier (LNA) with noise figure of 2.4 dB is inserted between the jig and VNA below 100 MHz. Above 100 MHz, the LNA is bypassed by two semiconductor RF analog switches. The Permalloy calibration sample used in Ref. 1 was measured using this system under a bias field of 200 mT. The modified TC-Perm showed approximately 40 times smaller noise level at low frequencies than before modification, which allows it to seamlessly measure the permeability over 300 kHz – 44 GHz. Acknowledgement - This work is supported by MIC SCOPE JP195003002.

![](https://assets.underline.io/uploads/markdown_image/1/image/fec082390465e6e7aae541daa230f473.jpg)

Block diagram of TC-Perm after modification.

![](https://assets.underline.io/uploads/markdown_image/1/image/b6d1d01fc1c45c4be7f0f8a32ce8f82f.jpg)

Measurement result of a Py sample with and without LNA.

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