We acknowledge funding from the Harvard-Massachusetts Institute of Technology Health Sciences and Technology program, and the SITA Foundation Award from the Institute for Medical Engineering and Science.

1. 0D lumped-parameter model
<ol>
	<li>Simulation setup 
	NOTE: In the numerical solver environment (see the Table of Materials), construct the domain as shown in Figure 1. This is composed of the 4-chamber heart, the upper body, abdominal, lower body, and thoracic compartments, as well as the proximal vasculature, including the aorta, the pulmonary artery, and the superior and inferior venae cavae. The standard elements used in this simulation are part of the default hydraulic library. Details can be found in the Supplemental Files.
	<ol>
		<li>Navigate the hydraulics library to find the required elements: hydraulic pipeline, constant volume hydraulic chamber, linear resistance, centrifugal pump, check valve, variable area orifice, and the custom hydraulic fluid.
		<ol>
			<li>Drop the hydraulic pipeline elements into the workspace. 
			&#8203;NOTE: These account for frictional losses as well as wall compliance and fluid compressibility in blood vessels and heart chambers. Through this block, the pressure loss is calculated using the Darcy-Weisbach law, whereas the change in diameter due to wall compliance depends on the compliance proportionality constant, the luminal pressure, and the time constant. Finally, fluid compressibility is defined by the bulk modulus of the medium.</li>
			<li>Insert the constant volume hydraulic chamber elements to define wall compliance and fluid compressibility. 
			&#8203;NOTE: This block does not take into account pressure losses due to friction.</li>
			<li>Add the linear resistance elements to define resistance to flow. 
			NOTE: This is independent of the geometrical properties of the vasculature, analogously to the resistive element used in electrical analog Windkessel models. Other blocks, such as the centrifugal pump, the check valve, the variable-area orifice, and the custom hydraulic fluid elements should be inserted to generate the desired pressure input to the system, model the effects of heart valves on blood flow, and define the mechanical properties of blood. Through these elements, the behavior of the cardiovascular system in both physiology and disease can be fully captured. The input signal for the centrifugal pump can be found in Figure S1A.</li>
			<li>Model the contractility of each heart chamber through the custom variable-compliance compliance chamber element. 
			NOTE: This accepts compliance as a time-varying user-defined input signal and is based on the time-varying elastance model (Figure S1B-D).</li>
		</ol>
		</li>
		<li>Provide the parameters relative to each element, as shown in Table S1, also found in Rosalia et al.15</li>
		<li>Insert a Physical Signal (PS) Repeating Sequence element for each of the blocks that require a time-varying user-defined input signal: the LV pump, the variable-compliance compliance elements, and the variable-area orifice blocks. 
		NOTE: Input signals utilized for this simulation can be found in Figure S1.</li>
		<li>Select the default ODE 23t implicit solver and run the simulation for 100 s to reach a steady state.</li>
	</ol>
	</li>
</ol>
2. The FEA model
<ol>
	<li>Simulation setup 
	NOTE: The FEA Model utilizes a coupled electrical-mechanical analysis in sequence. In this model, the electrical analysis is conducted first; then the resulting electric potentials are used as the excitation source in the following mechanical analysis. Therefore, the simulation setup contains two work domains: the electrical (ELEC) and the mechanical (MECH) domains, that are predefined in the FEA simulation software (Table of Materials)18. Hence, the following section only describes the analysis workflow. The FEA model uses the following user subroutines HETVAL, VUANISOHYPER, and UAMP for the electrical and mechanical material modeling18.
	<ol>
		<li>Navigate the ELEC domain to perform electrical analysis using the predefined temperature procedure in the Standard module.
		<ol>
			<li>Use a single-analysis step named BEAT. Set the duration of the cardiac cycle to 500 ms and apply an electrical potential pulse to a node set representing the sinoatrial (SA) node (node set: R_Atrium-1.SA_NODE).</li>
			<li>Review the default electrical waveform, which ranges from -80 mV to 20 mV over 200 ms with the smooth step amplitude definition, as described in the model guide18. Use the default values of material constants in the electrical analysis to adjust the AV delay.</li>
			<li>Launch the Job module, and create a job named heart-elec.</li>
		</ol>
		</li>
		<li>Once the electrical analysis setup is completed, navigate the MECH domain to perform the fluid cavity-based mechanical analysis. 
		NOTE: The mechanical simulation is performed after the electrical analysis, and the resulting electric potentials are used as the excitation source for the mechanical analysis. The mechanical analysis contains multiple steps.
		<ol>
			<li>Use the three main steps named PRE-LOAD, BEAT1, AND RECOVERY1. In the PRE-LOAD step, review the boundary conditions of the pre-stressed state of the heart. Use 0.3 s as the step time to linearly ramp up the pressure in the fluid chambers. 
			NOTE: The predefined fluid cavity pressure values are shown in Table S3. The pre-stressed state of the heart was already defined in the normal heart simulation setup, and the initial node conditions are provided in the external simulation files, as listed in Table S5. Recalculation of the zero-stress state using the inverse mechanical simulation is required whenever the boundary condition is modified, as explained in steps 3.2.2-3.2.4.</li>
			<li>In the BEAT1 step, use 0.5 s as the step time to simulate contraction.</li>
			<li>In the RECOVERY1 step, select 0.5 s for cardiac relaxation and ventricular filling for a heart rate of 60 bpm.</li>
			<li>Enable the subsequent steps, BEATX and RECOVERYX, to simulate more than one cardiac cycle to reach a steady state. 
			NOTE: Three cardiac cycles will be sufficient to reach steady state. One cycle of the simulation is completed in ~8 h on a 24-core processor (3.2 GHz x 24).</li>
			<li>Launch the Job module, and create a job named heart-mech, enabling the double precision option.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Review the simplified lumped-parameter Windkessel model 
	NOTE: The mechanical domain of the FEA model has a blood flow model, which is based on a simplified lumped-parameter circuit and is created as a combination of surface-based fluid cavities and fluid exchanges as seen in Figure 218.
	<ol>
		<li>Use the Windkessel representation mentioned in the above note to run the simulation. Review the blood flow model representation to adjust the values of the resistive and capacitive elements for flow resistances and structural compliances, respectively.</li>
		<li>Review the 3D finite element representation of four heart chambers, and ensure their geometrical positions are accurate.</li>
		<li>Check the heart assembly, and switch to the Interaction module to adjust the compliance and contractility values of each of the four heart chambers. 
		NOTE: The default values in the Interaction module are configured to simulate an idealized healthy human heart beat cycle18.</li>
		<li>Review the following hydrostatic fluid cavities in the Interaction module, CAV-AORTA, CAV-LA, CAV-LV, CAV-PULMONARY_TRUNK, CAV-RA, CAV-RV, CAV-SVC, CAV-ARTERIAL-COMP, CAV-PULMONARY-COMP, and CAV-VENOUS-COMP (Table S3).</li>
		<li>Use the compliance chambers (CAV-ARTERIAL-COMP, CAV-PULMONARY-COMP, and CAV-VENOUS-COMP) as cubic volumes as they represent the compliance of the arterial, venous, and pulmonary circulations.</li>
		<li>Attach three compliance cubic volumes to a grounded spring, and review the stiffness value to model the pressure-volume response in the arterial, venous, and pulmonary circulations.</li>
		<li>Check the following fluid exchange definitions between the hydrostatic fluid cavities: arterial-venous, venous-right atrium, right atrium-right ventricle, right ventricle-pulmonary system, pulmonary system-left atrium, left atrium-left ventricle, and left ventricle-aorta (Table S4).</li>
		<li>Adjust the viscous resistance coefficient to modify the blood flow model in each fluid exchange link (see Supplemental files for more information about the viscous resistance effect).</li>
	</ol>
	</li>
	<li>Multiphysics simulation
	<ol>
		<li>Locate the CAE database file in the working directory. 
		NOTE: The FEA Model in this protocol is delivered in the database and is named as LH-Human-Model-Beta-V2_1.cae.</li>
		<li>Insert the input, object, and library files to the working directory to run the simulation. See Table S5 for the full list of input and library files.</li>
		<li>Launch the FEA model simulation software (see the Table of Materials). 
		NOTE: Consult the software provider for compatibility with later versions18.</li>
		<li>Review the parts, assembly, and boundary conditions in both ELEC and MECH domains, as described in sections 2.2 and 2.3.</li>
		<li>First, run the electrical simulation job named heart-elec, as described in section 2.1.1.3. Visually inspect the electrical potential results to verify that the heart-elec simulation ran as expected. Then, ensure that the result file heart-elec.odb is in the working directory.</li>
		<li>Move to the second simulation phase by switching to the MECH domain. Review the values of the material constants used in the mechanical simulation to model the desired passive and active cardiac response.</li>
		<li>Ensure that the material library files for the mechanical analysis use the HYBRID- string name. To modify the material response of the heart chambers, adjust the appropriate hybrid material file, or replace the entire material response by defining a new material behavior in the Materials section in the CAE module. 
		NOTE: Detailed information about the built-in constitutive laws can be found in the user guide18.</li>
		<li>In the PRE-LOAD step, set the pressures of the hydrostatic cavities to obtain the desired physiologic behavior. Use the built-in smooth amplitude option to ramp up from zero to the desired pressure level as described in step 2.1.2.1.</li>
		<li>Disable the pressure boundary conditions defined in 2.1.2.1 to run the blood flow model with a constant overall blood volume within the circulation system. Run the simulation job named heart-mech, as described in section 2.1.2.5.</li>
	</ol>
	</li>
</ol>
3. Aortic valve stenosis
NOTE: Aortic stenosis is often a driver of HFpEF as it leads to pressure overload and ultimately, to concentric remodeling and compliance loss of the left ventricular wall. The hemodynamics observed in aortic stenosis often progress to those seen in HFpEF.
<ol>
	<li>The lumped-parameter model
	<ol>
		<li>Modify the input signal in the PS repeating sequence element relative to the aortic valve, located in the left ventricular compartment. Simulate a reduction of the orifice area equal to 70% compared to baseline (Table S6). 
		NOTE: The input values will represent the orifice area of the stenotic valve during each heartbeat. The orifice area value can be easily adjusted by multiplying the start output values vector of the aortic valve PS element by a decimal value corresponding to the final orifice area with respect to its original value. In this work, a factor of 0.3 was used to achieve 70% constriction.</li>
	</ol>
	</li>
	<li>The FEA model
	<ol>
		<li>Modify the fluid exchange definition of the LINK-LV-ARTERIAL parameter. 
		NOTE: This parameter possesses a viscous resistance coefficient tuned to the blood flow between the left ventricle and the aorta. The effective exchange area can be modified to adjust the blood flow and create the appropriate aortic stenosis model (Table S7).</li>
		<li>Locate the toolbox folder and copy the files inside that folder to the main working directory.</li>
		<li>Perform an inverse mechanical simulation by executing the toolbox files18. To this end, change the suction pressures of the left ventricle and left atrium to 6 mmHg in the fluid cavity to adjust their initial volumetric state for the aortic stenosis model. Execute the inversePreliminary.py function. 
		&#8203;NOTE:Recalculation of the zero-stress state using the inverse mechanical simulation is required whenever the boundary condition is modified.</li>
		<li>Once the inverse mechanical simulation is completed, run the post-processing functions: calcNodeCoords.py and straight_mv_chordae.py. Use the default values for the other flow parameters, and run a new mechanical simulation as described in section 2.1.2.5.</li>
	</ol>
	</li>
</ol>
4. HFpEF hemodynamics
NOTE: To simulate the effects of chronic remodeling, the mechanical properties of the left heart were modified.
<ol>
	<li>The lumped-parameter model
	<ol>
		<li>Modify the left ventricular diastolic compliance of the LV compliance element to mimic wall stiffening due to pressure-overload, using the value of end-diastolic compliance in Table S8. 
		NOTE: Assume compliance to drop linearly from end-systole to end-diastole.</li>
		<li>Increase the leak resistance of the LV pump to 18 &#215; 106 Pa s m-3 (Table S8) to capture the elevated left ventricular pressures observed in HFpEF.</li>
	</ol>
	</li>
	<li>The FEA model
	<ol>
		<li>Edit the active material properties of the left ventricle geometry. Increase the stiffness component to tune the active tissue response affecting the stress components in the fiber and sheet directions in the constitutive model.
		<ol>
			<li>Modify the material response of the left ventricle in the mech-mat-LV_ACTIVE file. 
			&#8203;NOTE: The magnitude of stiffness for the left ventricular chamber can be tuned to provide the appropriate diastolic compliance effects.</li>
			<li>Increase the stiffness parameters a and b in the anisotropic hyperelastic formulation to capture the increased stiffness response for the HFpEF physiology.</li>
			<li>In the PRE-LOAD step, set the fluid cavity pressures of the left ventricle and left atrium to 20 mmHg.</li>
			<li>Perform an inverse mechanical simulation to obtain the volumetric state of the left ventricle and atrium. Export the nodal coordinates from the heart-mech-inverse.odb file18.</li>
			<li>Execute the post-processing functions: calcNodeCoords.py and straight_mv_chordae.py, as described in step 3.2.4. Locate the new nodal input files in the working directory and perform a new mechanical simulation, as described in section 2.1.2.5.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

There are no conflicts of interest associated with this work.

The lumped-parameter and FEA platforms proposed in this work recapitulated the cardiovascular hemodynamics under physiologic conditions, both in the acute phase of stenosis-induced pressure overload and in chronic HFpEF. By capturing the role that pressure overload plays in the acute and chronic phases of HFpEF development, the results from these models are in agreement with the clinical literature of HFpEF, including the onset of a transaortic pressure gradient due to aortic stenosis, an increase in the left ventricular...

Heart failure is a leading cause of death worldwide, which occurs when the heart is unable to pump or fill adequately to keep up with the metabolic demands of the body. The ejection fraction, i.e., the relative amount of blood stored in the left ventricle that is ejected with each contraction is used clinically to classify heart failure into (i) heart failure with reduced ejection fraction (HFrEF) and (ii) heart failure with preserved ejection fraction (HFpEF), for ejection fractions less than or greater than 45%, respectively1,2,3. Symptoms of HFpEF often develop in response to left ventricular pressure overload, which can be caused by several conditions including aortic stenosis, hypertension, and left ventricular outflow tract obstruction3,4,5,6,7. Pressure overload drives a cascade of molecular and cellular aberrations, leading to thickening of the left ventricular wall (concentric remodeling) and ultimately, to wall stiffening or loss of compliance8,9,10. These biomechanical changes profoundly affect cardiovascular hemodynamics as they result in an elevated end-diastolic pressure-volume relationship and in a reduction of the end-diastolic volume11.
Computational modeling of the cardiovascular system has advanced the understanding of blood pressures and flows in both physiology and disease and has fostered the development of diagnostic and therapeutic strategies12. In silico&#160;models are classified into low- or high-dimensional models, with the former utilizing analytical methods to evaluate global hemodynamic properties with low computational demand and the latter providing a more extensive multiscale and multiphysics description of cardiovascular mechanics and hemodynamics in the 2D or 3D domain13. The lumped-parameter Windkessel representation is the most common among the low-dimensional descriptions. Based on the electrical circuit analogy (Ohm&#39;s law), this mimics the overall hemodynamic behavior of the cardiovascular system through a combination of resistive, capacitive, and inductive elements14. A recent study by this group has proposed an alternative Windkessel model in the hydraulic domain that allows the modeling of changes in the geometry and mechanics of large vessels-heart chambers and valves-in a more intuitive way than traditional electrical analog models. This simulation is developed on an object-oriented numerical solver (see the Table of Materials) and can capture the normal hemodynamics, physiologic effects of cardiorespiratory coupling, respiratory-driven blood flow in single-heart physiology, and hemodynamic changes due to aortic constriction. This description expands upon the capabilities of lumped-parameter models by offering a physically intuitive approach to model a spectrum of pathologic conditions including heart failure15.
High-dimensional models are based on FEA to compute spatiotemporal hemodynamics and fluid-structure interactions. These representations can provide detailed and accurate descriptions of the local blood flow field; however, due to their low computational efficiency, they are not suitable for studies of the entire cardiovascular tree16,17. A software package (see the Table of Materials) was employed as an anatomically accurate FEA platform of the 4-chamber adult human heart, which integrates the electro-mechanical response, structural deformations, and fluid cavity-based hemodynamics. The adapted human heart model also comprises a simple lumped-parameter model that defines the flow exchange among the different fluid cavities, as well as a complete mechanical characterization of the cardiac tissue18,19.
Several lumped-parameter and FEA models of heart failure have been formulated to capture hemodynamic abnormalities and evaluate therapeutic strategies, particularly in the context of mechanical circulatory assist devices for HFrEF20,21,22,23,24. A broad array of 0D lumped-parameter models of various complexities has therefore successfully captured the hemodynamics of the human heart in physiological and HFrEF conditions via optimization of two or three-element electrical analog Windkessel systems20,21,23,24. The majority of these representations are uni- or biventricular models based on the time varying-elastance formulation to reproduce the contractile action of the heart and use a non-linear end-diastolic pressure-volume relationship to describe ventricular filling25,26,27. Comprehensive models, which capture the complex cardiovascular network and mimic both the atrial and ventricular pumping action, have been used as platforms for device testing. Nevertheless, although a significant body of literature exists around the field of HFrEF, very few in silico models of HFpEF have been proposed20,22,28,29,30,31.
A low-dimensional model of HFpEF hemodynamics, recently developed by Burkhoff et al.32 and Granegger et al.28, can capture the pressure-volume (PV) loops of the 4-chamber heart, fully recapitulating the hemodynamics of various phenotypes of HFpEF. Furthermore, they utilize their in silico platform to evaluate the feasibility of a mechanical circulatory device for HFpEF, pioneering computational research of HFpEF for physiology studies as well as device development. However, these models remain unable to capture the dynamic changes in blood flows and pressures observed during disease progression. A recent study by Kadry et al.30 captures the various phenotypes of diastolic dysfunction by adjusting the active relaxation of the myocardium and the passive stiffness of the left ventricle on a low-dimensional model. Their work provides a comprehensive hemodynamic analysis of diastolic dysfunction based on both the active and passive properties of the myocardium. Similarly, the literature of high-dimensional models has primarily focused on HFrEF19,33,34,35,36,37. Bakir et al.33 proposed a fully-coupled cardiac fluid-electromechanics FEA model to predict the HFrEF hemodynamic profile and the efficacy of a left-ventricular assist device (LVAD). This biventricular (or two-chamber) model utilized a coupled Windkessel circuit to simulate the hemodynamics of the healthy heart, HFrEF and HFrEF with LVAD support33,37.
Similarly, Sack et al.35 developed a biventricular model to investigate right ventricular dysfunction. Their biventricular geometry was obtained from a patient&#39;s magnetic resonance imaging (MRI) data, and the model&#39;s finite-element mesh was constructed using image segmentation to analyze the hemodynamics of a VAD-supported failing right ventricle35. Four-chamber FEA cardiac approaches have been developed to enhance the accuracy of models of the electromechanical behavior of the heart19,34. In contrast to biventricular descriptions, MRI-derived four-chamber models of the human heart provide a better representation of the cardiovascular anatomy18. The heart model employed in this work is an established example of a four-chamber FEA model. Unlike lumped-parameter and biventricular FEA models, this representation captures hemodynamic changes as they occur during disease progression34,37. Genet et al.34, for example, used the same platform to implement a numerical growth model of the remodeling observed in HFrEF and HFpEF. However, these models evaluate the effects of cardiac hypertrophy on structural mechanics only and do not provide a comprehensive description of the associated hemodynamics.
To address the lack of HFpEF in silico&#160;models in this work, the lumped-parameter model previously developed by this group15 and the FEA model were readapted to simulate the hemodynamic profile of HFpEF. To this end, the ability of each model to simulate cardiovascular hemodynamics at baseline will be first demonstrated. The effects of stenosis-induced left ventricular pressure overload and of diminished left ventricular compliance due to cardiac remodeling-a typical hallmark of HFpEF-will then be evaluated.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Abaqus Software</td>
			<td>Dassault Syst&#232;mes Simulia Corp.</td>
			<td></td>
			<td>Version used: 2018; FEA simulation software</td>
		</tr>
		<tr>
			<td>HETVAL</td>
			<td>Dassault Syst&#232;mes Simulia Corp.</td>
			<td></td>
			<td>Version used: 2018</td>
		</tr>
		<tr>
			<td>Hydraulic (Isothermal) library</td>
			<td>MathWorks</td>
			<td></td>
			<td>Version used: 2020a</td>
		</tr>
		<tr>
			<td>Living Heart Human Model</td>
			<td>Dassault Syst&#232;mes Simulia Corp.</td>
			<td></td>
			<td>Version used: V2_1, anatomically accurate FEA platform of 4-chamber adult human heart</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td></td>
			<td rowspan="2">Version used: 2020a, object-oriented numerical solver</td>
		</tr>
		<tr>
			<td>SIMSCAPE FLUIDS</td>
			<td>MathWorks</td>
			<td></td>
		</tr>
		<tr>
			<td>UAMP</td>
			<td>Dassault Syst&#232;mes Simulia Corp.</td>
			<td></td>
			<td>Version used: 2018</td>
		</tr>
		<tr>
			<td>VUANISOHYPER</td>
			<td>Dassault Syst&#232;mes Simulia Corp.</td>
			<td></td>
			<td>Version used: 2018</td>
		</tr>
	</tbody>
</table>

lumped-parameter and finite element modeling of heart failure with preserved ejection fraction

Scientific efforts in the field of computational modeling of cardiovascular diseases have largely focused on heart failure with reduced ejection fraction (HFrEF), broadly overlooking heart failure with preserved ejection fraction (HFpEF), which has more recently become a dominant form of heart failure worldwide. Motivated by the paucity of HFpEF in silico representations, two distinct computational models are presented in this paper to simulate the hemodynamics of HFpEF resulting from left ventricular pressure overload. First, an object-oriented lumped-parameter model was developed using a numerical solver. This model is based on a zero-dimensional (0D) Windkessel-like network, which depends on the geometrical and mechanical properties of the constitutive elements and offers the advantage of low computational costs. Second, a finite element analysis (FEA) software package was utilized for the implementation of a multidimensional simulation. The FEA model combines three-dimensional (3D) multiphysics models of the electro-mechanical cardiac response, structural deformations, and fluid cavity-based hemodynamics and utilizes a simplified lumped-parameter model to define the flow exchange profiles among different fluid cavities. Through each approach, both the acute and chronic hemodynamic changes in the left ventricle and proximal vasculature resulting from pressure overload were successfully simulated. Specifically, pressure overload was modeled by reducing the orifice area of the aortic valve, while chronic remodeling was simulated by reducing the compliance of the left ventricular wall. Consistent with the scientific and clinical literature of HFpEF, results from both models show (i) an acute elevation of transaortic pressure gradient between the left ventricle and the aorta and a reduction in the stroke volume and (ii) a chronic decrease in the end-diastolic left ventricular volume, indicative of diastolic dysfunction. Finally, the FEA model demonstrates that stress in the HFpEF myocardium is remarkably higher than in the healthy heart tissue throughout the cardiac cycle.

Results from the baseline simulations are illustrated in Figure 3. This depicts the pressure and volume waveforms of the left ventricle and the aorta (Figure 3A) as well as the left ventricular PV loop (Figure 3B). The two in silico models show similar aortic and left ventricular hemodynamics, which are within the physiologic range. Minor differences in the response predicted by the two platforms can be noticed during the ventricula...

Watch this Scientific Journal Video about Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction at JoVE.com

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

This work introduces two computational models of heart failure with preserved ejection fraction based on a lumped-parameter approach and finite element analysis. These models are used to evaluate the changes in the hemodynamics of the left ventricle and related vasculature induced by pressure overload and diminished ventricular compliance.

Scientific efforts in the field of computational modeling of cardiovascular diseases have largely focused on heart failure with reduced ejection fraction ...

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6.2K Views.  Massachusetts Institute of Technology. This work introduces two computational models of heart failure with preserved ejection fraction based on a lumped-parameter approach and finite element analysis. These models are used to evaluate the changes in the hemodynamics of the left ventricle and related vasculature induced by pressure overload and diminished ventricular compliance. 

Video: Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

In Situ Time-dependent Dielectric Breakdown in the Transmission Electron Microscope: A Possibility to Understand the Failure Mechanism in Microelectronic Devices

The time-dependent dielectric breakdown (TDDB) in on-chip interconnect stacks is one of the most critical failure mechanisms for microelectronic devices. The aggressive scaling of feature sizes, both on devices and interconnects, leads to serious challenges to ensure the required product reliability. Standard reliability tests and post-mortem failure analysis provide only limited information about the physics of failure mechanisms and degradation kinetics. Therefore it is necessary to develop new experimental approaches and procedures to study the TDDB failure mechanisms and degradation kinetics in particular. In this paper, an in situ experimental methodology in the transmission electron microscope (TEM) is demonstrated to investigate the TDDB degradation and failure mechanisms in Cu/ULK interconnect stacks. High quality imaging and chemical analysis are used to study the kinetic process. The in situ electrical test is integrated into the TEM to provide an elevated electrical field to the dielectrics. Electron tomography is utilized to characterize the directed Cu diffusion in the insulating dielectrics. This experimental procedure opens a possibility to study the failure mechanism in interconnect stacks of microelectronic products, and it could also be extended to other structures in active devices.

In Situ Time-dependent Dielectric Breakdown in the Transmission Electron Microscope: A Possibility to Understand the Failure Mechanism in Microelectronic Devices

The time-dependent dielectric breakdown (TDDB) in on-chip interconnect stacks is one of the most critical failure mechanisms for microelectronic devices. This paper demonstrates the procedure of an in situ TDDB experiment in the transmission electron microscope, which opens a possibility to study the failure mechanism in microelectronic products.

The time-dependent dielectric breakdown (TDDB) in on-chip interconnect stacks is one of the most critical failure mechanisms for microelectronic devices. ...

Fabrication of High Contrast Gratings for the Spectrum Splitting Dispersive Element in a Concentrated Photovoltaic System

High contrast gratings are designed and fabricated and its application is proposed in a parallel spectrum splitting dispersive element that can improve the solar conversion efficiency of a concentrated photovoltaic system. The proposed system will also lower the solar cell cost in the concentrated photovoltaic system by replacing the expensive tandem solar cells with the cost-effective single junction solar cells. The structures and the parameters of high contrast gratings for the dispersive elements were numerically optimized. The large-area fabrication of high contrast gratings was experimentally demonstrated using nanoimprint lithography and dry etching. The quality of grating material and the performance of the fabricated device were both experimentally characterized. By analyzing the measurement results, the possible side effects from the fabrication processes are discussed and several methods that have the potential to improve the fabrication processes are proposed, which can help to increase the optical efficiency of the fabricated devices.

The fabrication of high contrast gratings as the parallel spectrum splitting dispersive element in a concentrated photovoltaic system is demonstrated. Fabrication processes including nanoimprint lithography, TiO2 sputtering and reactive ion etching are described. Reflectance measurement results are used to characterize the optical performance.

High contrast gratings are designed and fabricated and its application is proposed in a parallel spectrum splitting dispersive element that can improve ...

Failure Analysis of Batteries Using Synchrotron-based Hard X-ray Microtomography

Imaging morphological changes that occur during the lifetime of rechargeable batteries is necessary to understand how these devices fail. Since the advent of lithium-ion batteries, researchers have known that the lithium metal anode has the highest theoretical energy density of any anode material. However, rechargeable batteries containing a lithium metal anode are not widely used in consumer products because the growth of lithium dendrites from the anode upon charging of the battery causes premature cell failure by short circuit. Lithium dendrites can also form in commercial lithium-ion batteries with graphite anodes if they are improperly charged. We demonstrate that lithium dendrite growth can be studied using synchrotron-based hard X-ray microtomography. This non-destructive imaging technique allows researchers to study the growth of lithium dendrites, in addition to other morphological changes inside batteries, and subsequently develop methods to extend battery life.

Synchrotron-based hard X-ray microtomography is used to image the electrochemical growth of dendrites from a lithium metal electrode through a solid polymer electrolyte membrane.

Imaging morphological changes that occur during the lifetime of rechargeable batteries is necessary to understand how these devices fail. Since the advent ...

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data acquisition times with high spatial resolution. In the electronics industry there is serious interest in performing failure analysis on 3D microelectronic packages, many which contain multiple levels of high-density interconnections. Often in tomography there is a trade-off between image resolution and the volume of a sample that can be imaged. This inverse relationship limits the usefulness of conventional computed tomography (CT) systems since a microelectronic package is often large in cross sectional area 100-3,600 mm2, but has important features on the micron scale. The micro-tomography beamline at the Advanced Light Source (ALS), in Berkeley, CA USA, has a setup which is adaptable and can be tailored to a sample&#39;s properties, i.e., density, thickness, etc., with a maximum allowable cross-section of 36 x 36 mm. This setup also has the option of being either monochromatic in the energy range ~7-43 keV or operating with maximum flux in white light mode using a polychromatic beam. Presented here are details of the experimental steps taken to image an entire 16 x 16 mm system within a package, in order to obtain 3D images of the system with a spatial resolution of 8.7 &#181;m all within a scan time of less than 3 min. Also shown are results from packages scanned in different orientations and a sectioned package for higher resolution imaging. In contrast a conventional CT system would take hours to record data with potentially poorer resolution. Indeed, the ratio of field-of-view to throughput time is much higher when using the synchrotron radiation tomography setup. The description below of the experimental setup can be implemented and adapted for use with many other multi-materials.

For this study synchrotron radiation micro-tomography, a non-destructive three-dimensional imaging technique, is employed to investigate an entire microelectronic package with a cross-sectional area of 16 x 16 mm. Due to the synchrotron&#39;s high flux and brightness the sample was imaged in just 3 min&#160;with an 8.7 &#181;m spatial resolution.

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data ...

In Vitro Culture of Epicardial Cells From Mouse Embryonic Heart

During embryogenesis, the epicardial contribution to coronary vasculature development has been very well established. Cells derived from the epicardium differentiate into smooth muscle cells, fibroblasts and endothelial cells that contribute to the formation of coronary vessels. Here we have established an in vitro culture method for embryonic epicardial cells. Using genetic labelling, we have demonstrated that the majority of the migrating cells in our explant culture are of epicardial origin. Epicardial explant cells also retain the expression of epicardial markers (Wt1 and Tbx18). Furthermore, we provide evidence that epicardial explant cells undergo epithelial to mesenchymal transition (EMT), migrate and differentiate into smooth muscle cells after Transforming growth factor beta 1 (TGF-&#946;1) treatment in a manner indistinguishable from that of epicardial cells in vivo. In conclusion, we provide a novel method for the culture of embryonic epicardial cells, which will help to explore the role of specific genes in epicardial cell biology.

In Vitro Culture of Epicardial Cells From Mouse Embryonic Heart

The epicardium is an essential source of multipotent cardiovascular progenitor cells and paracrine factors that are required for cardiovascular development and regeneration. We describe here a method to culture mouse embryonic epicardial cells.

During embryogenesis, the epicardial contribution to coronary vasculature development has been very well established. Cells derived from the epicardium ...

A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy

Flexible non-volatile memories have received much attention as they are applicable for portable smart electronic device in the future, relying on high-density data storage and low-power consumption capabilities. However, the high-quality oxide based nonvolatile memory on flexible substrates is often constrained by the material characteristics and the inevitable high-temperature fabrication process. In this paper, a protocol is proposed to directly grow an epitaxial yet flexible lead zirconium titanate memory element on muscovite mica. The versatile deposition technique and measurement method enable the fabrication of flexible yet single-crystalline non-volatile memory elements necessary for the next generation of smart devices.

In this paper, we present a protocol to directly grow an epitaxial yet flexible lead zirconium titanate memory element on muscovite mica.

Flexible non-volatile memories have received much attention as they are applicable for portable smart electronic device in the future, relying on ...

Enhanced Electron Injection and Exciton Confinement for Pure Blue Quantum-Dot Light-Emitting Diodes by Introducing Partially Oxidized Aluminum Cathode

Stable and efficient red (R), green (G), and blue (B) light sources based on solution-processed quantum dots (QDs) play important roles in next-generation displays and solid-state lighting technologies. The brightness and efficiency of blue QDs-based light-emitting diodes (LEDs) remain inferior to their red and green counterparts, due to the inherently unfavorable energy levels of different colors of light. To solve these problems, a device structure should be designed to balance the injection holes and electrons into the emissive QD layer. Herein, through a simple autoxidation strategy, pure blue QD-LEDs which are highly bright and efficient are demonstrated, with a structure of ITO/PEDOT:PSS/Poly-TPD/QDs/Al:Al2O3. The autoxidized Al:Al2O3 cathode can effectively balance the injected charges and enhance radiative recombination without introducing an additional electron transport layer (ETL). As a result, high color-saturated blue QD-LEDs are achieved with a maximum luminance over 13,000 cd m-2, and a maximum current efficiency of 1.15 cd A-1. The easily controlled autoxidation procedure paves the way for achieving high-performance blue QD-LEDs.

A protocol is presented for fabricating high-performance, pure blue ZnCdS/ZnS-based quantum dots light-emitting diodes by employing an autoxidized aluminum cathode.

Stable and efficient red (R), green (G), and blue (B) light sources based on solution-processed quantum dots (QDs) play important roles in next-generation ...

A Rapid Method for Modeling a Variable Cycle Engine

The variable cycle engines (VCE) that combine the advantages of turbofan and turbojet engines, are widely considered to be the next generation aircraft engines. However, developing VCE requires high costs. Thus, it is essential to build a mathematical model when developing an aircraft engine, which may avoid a large number of real tests and reduce the cost dramatically. Modeling is also crucial in control law development. In this article, based on a graphical simulation environment, a rapid method for modeling a double bypass variable cycle engine using object-oriented modeling technology and modular hierarchical architecture is described. Firstly, the mathematical model of each component is built based on the thermodynamic calculation. Then, a hierarchical engine model is built via the combination of each component mathematical model and the N-R solver module. Finally, the static and dynamic simulations are carried out in the model and the simulation results prove the effectiveness of the modeling method. The VCE model built through this method has the advantages of clear structure and real-time observation.

Here, we present a protocol to build a component-level mathematical model for a variable cycle engine.

The variable cycle engines (VCE) that combine the advantages of turbofan and turbojet engines, are widely considered to be the next generation aircraft ...

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

The rapid development of X-ray imaging techniques with image processing and analysis skills has enabled the acquisition of CT images of granular soils with high-spatial resolutions. Based on such CT images, grain-scale mechanical behavior such as particle kinematics (i.e., particle translations and particle rotations), strain localization and inter-particle contact evolution of granular soils can be quantitatively investigated. However, this is inaccessible using conventional experimental methods. This study demonstrates the exploration of the grain-scale mechanical behavior of a granular soil sample under triaxial compression using synchrotron X-ray micro-tomography (&#956;CT). With this method, a specially fabricated miniature loading apparatus is used to apply confining and axial stresses to the sample during the triaxial test. The apparatus is fitted into a synchrotron X-ray tomography setup so that high-spatial resolution CT images of the sample can be collected at different loading stages of the test without any disturbance to the sample. With the capability of extracting information at the macro scale (e.g., sample boundary stresses and strains from the triaxial apparatus setup) and the grain scale (e.g., grain movements and contact interactions from the CT images), this procedure provides an effective methodology to investigate the multi-scale mechanics of granular soils.

The protocol describes procedures to acquire high-spatial resolution computed tomography (CT) images of a granular soil during triaxial compression, and to apply image processing techniques to these CT images to explore the grain-scale mechanical behavior of the soil under loading.

The rapid development of X-ray imaging techniques with image processing and analysis skills has enabled the acquisition of CT images of granular soils ...

Modeling and Experimental Analysis of the Single-Shaft Coaxial Motor-Pump Assembly in Electrohydrostatic Actuators

An electrohydrostatic actuator (EHA) can be the most promising alternative compared with the traditional hydraulic servo actuators for its high power density, ease of maintenance, and reliability. As the core power unit that determines the performance and service life of the EHA, the motor-pump assembly should simultaneously possess a wide speed/pressure range and a high dynamic response.
This paper presents a method to test the performance of the motor-pump assembly through simulation and experimentation. The flow output characteristics were defined through simulation and analysis of the assembly at the beginning of the experiment, leading to the conclusion of whether the pump could meet the requirements of the EHA. A series of performance tests were conducted on the motor-pump assembly via a pump test bench in the speed range of 1,450-9,000 rpm and the pressure range of 1-30 MPa.
We tested the overall efficiency of the motor-pump assembly under various working conditions after confirming the consistency between the test results of the flow output characteristics with the simulation results. The results showed that the assembly has higher overall efficiency when working at 4,500-7,000 rpm under the pressure of 10-25 MPa and at 2,000-2,500 rpm under 5-15 MPa. Overall, this method can be utilized for determining in advance whether the motor-pump assembly meets the requirements of EHA. Moreover, this paper proposes a rapid test method of the motor-pump assembly in various working conditions, which could assist in predicting EHA performance.

We built a simulation model to evaluate pump flow characteristics and performance of the single-shaft coaxial motor-pump assembly in electrohydrostatic actuators and investigate the overall efficiency in a wide set of working conditions of the motor-pump assembly experimentally.

An electrohydrostatic actuator (EHA) can be the most promising alternative compared with the traditional hydraulic servo actuators for its high power ...