Sabra Brucker works as an executive assistant. Her husband, Dagan, is a fifth-generation farmer in Cropsey, Illinois, about 100 miles south of Chicago.

After many years of infertility and miscarriages, they finally became the parents of four young children: Addison, born in 2017; Andi, born in 2019; and twins Aiden and Arie, born prematurely in March 2021.

The Brucker family had never previously endured a run-in with child protective services. A series of medical complications involving the younger twin, Aiden, suddenly changed that. After the parents sought care for their sick child, they were falsely accused of breaking Aiden's ribs and subjected to months of humiliating inequity. And when that was over, the authorities refused to disclose the identity of the actual perpetrator.

"I never thought that this was even humanly possible," says Sabra. "To be honest, I
was probably naive."

When Aiden was 5 months old, the Bruckers discovered he had genetic intestinal malrotation—the same condition that had required emergency surgery to save his older sister Addison's life back when she too was 5 months old.

On August 9, 2021, the Bruckers took Aiden to the OSF Children's Hospital Emergency Room in Peoria, Illinois. He was experiencing intense stomach pain and vomiting, just as his older sister had. Genetic intestinal malrotation can be a life-threatening condition, and it requires immediate, emergency intervention.

Aiden's condition, though serious, was not as immediately life-threatening as Addison's had been. He was given ultrasounds and X-rays for his upper GI track, abdomen, and chest. His intestinal reversal was visualized, but no skeletal concerns were noted. Nevertheless, he was held in the hospital for observation, and subjected to daily, repeated abdominal ultrasounds and chest and abdominal X-rays.

On the fourth day of his stay at the hospital, seven rib fractures became visible on the X-rays. These were all new, non-calcified fractures that had not appeared on earlier X-rays. Rib fractures are viewed by medical profession as evidence of possible abuse.

The Bruckers immediately suspected that the fractures had occurred during the hospital stay itself, possibly due to the extensive handling and exams Aiden had endured. The lack of any signs of these injuries at admission certainly suggested that they had appeared during Aiden's inpatient care. And yet as soon as the fractures were detected, a child abuse hotline call was placed to the Illinois Department of Children and Family Services (DCFS) naming the Bruckers as suspected abusers.

Sabra was in a meeting with her boss when she received the news.

"I immediately called my husband—he was at the hospital with Aiden—and I said, 'What is going on?'" Sabra recalls. "I just remember the sheer confusion and fear in his
voice."

Sabra and Dagan were not quick to point fingers, but they did wonder if the hospital was aware of its own potential liability when it accused them of causing the fractures.

Following the call to the child abuse hotline, a state-contracted child abuse pediatrician, Channing Petrak, assumed the role of directing Aiden's medical testing as a suspected child abuse victim. Petrak oversees child abuse cases under a subcontract her office holds with the DCFS for central Illinois. While not a hospital employee, she is viewed as the head of the hospital's child abuse team. In that capacity, she was empowered to decide which tests Aiden needed in order to confirm or rule out abuse.

She was also immediately enlisted to discuss the case with DCFS and the police and to determine whether child abuse had occurred. If she believed it had, her role would include testifying against the parents in the event the case went to court.

Petrak was responsible for testing not just Aiden but the other Brucker children as well. While parents have the right to refuse medical procedures that are not required by a court order or emergency, the fear of CPS retribution looms large.

On multiple occasions, Sabra requested a meeting with Petrak and the OSF team to ensure the timeline of the injuries was clear. She felt it necessary that everyone understand the fractures had not been present on Aiden's body upon admission, as shown by multiple X-ray examinations. Clarifying this, she thought, would allow her and Dagan to work alongside the hospital to identify their underlying cause.

Sabra even wrote on the whiteboard the team used for notes: "Can we clarify Xray finds with DCFS?" and snapped a photo of it.

"I wanted a picture with a time stamp because no one would speak to me," she says.

Sabra's requests were ignored.

Meanwhile, Petrak pushed the family to authorize an MRI, which would require Aiden to fast for eight hours and then undergo general anesthesia and be intubated. As there was no suspicion of other injuries that would have made an MRI useful, the Bruckers tried to object.

In response, the hospital threatened the family with a court order that would require Aiden to remain in the hospital's care pending a judicial order for the MRI. Since complying with the MRI demand seemed to be the only way to bring their son home quickly, Sabra comforted Aiden through the fast, and handed him over to the hospital's staff—who sedated and intubated him, and proceeded with the MRI.

The other Brucker children—ages four, two and now six months—were also subjected to observation at their home. These included visual exams of their genitals.

The state even demanded that the 4-year-old daughter, Addison, submit to a forensic interrogator. This investigator reported that Addison was very "sweet" and "polite," and no concerns were noted from her 2-hour interview.

Meanwhile, DCFS determined that the Bruckers could not take Aiden home by themselves upon his discharge. Instead, the agency demanded the family find someone else to take care of their four children. That person could do so at the Bruckers' home, and Sabra and Dagan could live there—but they would not be allowed to be alone with their children at any time. If they not did find a caregiver to watch the kids 24/7, the children would be taken into foster care and placed with strangers.

Sabra's parents, Don and Shari Boyd, lived 273 miles away. Thankfully, Shari was on hand to help out, even though she was in the middle of breast cancer treatment.

Diane Redleaf, a defense attorney who co-chairs the National Coalition to End Hidden Foster Care, says that the Bruckers' experience is commonplace. Efforts are underway to secure reforms that would allow families like the Bruckers to have some recourse when they are threatened with having their kids taken away.

This arrangement for the children was supposed to last for just two to five days, but DCFS kept extending it. The caseworker even reminded grandma Shari that she couldn't use the bathroom without taking the kids in with her. Sabra and Dagan's nighttime feedings of their baby twins also had to be supervised by Shari.

The Bruckers wanted to object, but they felt they had no choice.

This led to odd situations, such as Dagan not being able to have his kids take turns riding the combine with him—their favorite fall activity. The combine had only two seats, so if one of the children rode along, Shari and the other three children would have to somehow ride along too, or the government's plan would be violated.

As the weeks dragged on, the Bruckers worked to demonstrate that the abuse allegations against them were false. A University of Chicago pediatric orthopedic specialist, Christopher Sullivan, saw Aiden in his office and reviewed his radiology imaging and lab testing, formally concluding that the timing of the fractures' first appearance made it impossible for them to have occurred prior to the hospital admission.

Sullivan also noticed that Aiden had very low Vitamin D and high parathyroid hormone levels, which made his bones extremely fragile. He concluded that the likeliest explanation for the fractures was routine handling at the hospital.

Despite this report—and many letters from the Bruckers' pediatrician, family members, friends, and teachers—DCFS's restrictions persisted.

Meanwhile, DCFS came to suspect that the Bruckers' day care providers were Aiden's possible abuse perpetrators. For that reason, DCFS told the Bruckers they could no longer send their kids there. Everyone who had ever been in contact with Aiden before his hospital stay had suddenly become a suspect.

Sabra requested that their two older children be allowed to keep going to their day care— with their familiar friends and routines—but the caseworker said no. The caseworker also continued to demand weekly check-ins with the Bruckers. Each time, she insisted on strip-searching the twins and commenting on natural bodily features, such as inverted nipples.

As the family languished, Sabra checked the mail one day and was shocked to find a bill from the hospital for over $60,000. Her private insurance provider had denied the payment for Aiden's MRI as "medically unnecessary." The Bruckers told the hospital's billing department that they had not requested the MRI; it was done at the behest of Petrak. Soon after this, the Bruckers' billing records disappeared from their file at the hospital.

Illinois gives DCFS 60 days to complete an investigation. Knowing this, the Brucker family decided on day 60 that they had had enough of the "voluntary safety plan." They hired a lawyer with DCFS experience who confirmed their right to terminate the plan. He notified DCFS accordingly.

Three months later, in January 2022, a caseworker from a different DCFS regional office phoned Sabra to say their investigation file had been transferred. Since the children had not been seen by DCFS in several months, the new caseworker wanted to come observe them. The family declined this request. The new DCFS caseworker also informed Sabra that the Bruckers' case file was completely empty of investigative notes.

In March, and again in October, 14 months after the case had begun, the Bruckers' attorney submitted a complaint to the DCFS Inspector General. In November 2022, he received a response saying the inspector general was unable to investigate this complaint because the case was still open. The Bruckers couldn't help but wonder whether DCFS was keep the status of the investigation ambiguous in order to avoid accountability.

Finally, in November 2023, the Bruckers received a letter from DCFS stating that the case was now closed and Dagan and Sabra were cleared of any wrongdoing. Curiously, the letter claimed that "someone" had been "substantiated" as Aiden's abuser.

The Bruckers filed an inquiry as to who that person was. They were told they had no right to see these records.

Neither Petrak nor the hospital responded to a request for comment. A spokesperson for DCFS declared in a statement: "DCFS is mandated by Illinois statute to investigate any allegations of child abuse or neglect that is reported to our agency."

In situations like the Bruckers', which are far too numerous to be viewed as aberrations, concerns about children's health and well-being are cited as pretexts to legitimize witch hunts against parents and other caregivers. These investigations have lasting consequences. The Brucker children were left with extreme separation anxiety. Sabra experienced debilitating post-traumatic stress disorder. The family considered suing the caseworkers but decided that litigation would force them to relive the horror.

But they did decide to speak out about their harrowing experience. They want people to understand that the state's so-called voluntary safety plan did was neither voluntary nor safe—it was a sham.

Thankfully, Aiden's medical condition has resolved, and he's now in excellent physical shape.

"He's growing, cute, talking, very healthy now," says Sabra.

Meanwhile, Petrak recently became president of the board of directors of the National Children's Alliance. The organization oversees funding and accrediting for child advocacy centers, where allegedly abused children are interviewed and assessed across the country.

The post Illinois Falsely Accused These Parents of Abusing Their Baby—and Now Won't Tell Them Who Actually Did It appeared first on Reason.com.

For the past year, New York City-based political strategy firm Mercury Public Affairs has lobbied Chicago Mayor Brandon Johnson on economic and labor issues. And beginning in 2024, it has also consulted for his political fund. According to the Chicago Tribune, there are no state or city ethics rules that prohibit a political group from helping an elected official raise money at the same time it is lobbying him.

The post Brickbat: Double Dip appeared first on Reason.com.

Increasingly tight tolerances and rigorous demands for quality are forcing chipmakers and equipment manufacturers to ferret out minor process variances, which can create significant anomalies in device behavior and render a device non-functional.

In the past, many of these variances were ignored. But for a growing number of applications, that’s no longer possible. Even minor fluctuations in deposition rates during a chemical vapor deposition (CVD) process, for example, can lead to inconsistencies in layer uniformity, which can impact the electrical isolation properties essential for reliable circuit operation. Similarly, slight variations in a photolithography step can cause alignment issues between layers, leading to shorts or open circuits in the final device.

Some of these variances can be attributed to process error, but more frequently they stem from process drift — the gradual deviation of process parameters from their set points. Drift can occur in any of the hundreds of process steps involved in manufacturing a single wafer, subtly altering the electrical properties of chips and leading to functional and reliability issues. In highly complex and sensitive ICs, even the slightest deviations can cause defects in the end product.

“All fabs already know drift. They understand drift. They would just like a better way to deal with drift,” said David Park, vice president of marketing at Tignis. “It doesn’t matter whether it’s lithography, CMP (chemical mechanical polishing), CVD or PVD (chemical/physical vapor deposition), they’re all going to have drift. And it’s all going to happen at various rates because they are different process steps.”

At advanced nodes and in dense advanced packages, where a nanometer can be critical, controlling process drift is vital for maintaining high yield and ensuring profitability. By rigorously monitoring and correcting for drift, engineers can ensure that production consistently meets quality standards, thereby maximizing yield and minimizing waste.

“Monitoring and controlling hundreds of thousands of sensors in a typical fab requires the ability to handle petabytes of real-time data from a large variety of tools,” said Vivek Jain, principal product manager, smart manufacturing at Synopsys. “Fabs can only control parameters or behaviors they can measure and analyze. They use statistical analysis and error budget breakdowns to define upper control limits (UCLs) and lower control limits (LCLs) to monitor the stability of measured process parameters and behaviors.”

Dialing in legacy fabs
In legacy fabs — primarily 200mm — most of the chips use 180nm or older process technology, so process drift does not need to be as precisely monitored as in the more advanced 300mm counterparts. Nonetheless, significant divergence can lead to disparities in device performance and reliability, creating a cascade of operational challenges.

Manufacturers operating at older technology nodes might lack the sophisticated, real-time monitoring and control methods that are standard in cutting-edge fabs. While the latter have embraced ML to predict and correct for drift, many legacy operations still rely heavily on periodic manual checks and adjustments. Thus, the management of process drift in these settings is reactive rather than proactive, making changes after problems are detected rather than preventing them.

“There is a separation between 300-millimeter and 200-millimeter fabs,” said Park. “The 300-millimeter guys are all doing some version of machine learning. Sometimes it’s called advanced process control, and sometimes it’s actually AI-powered process control. For some of the 200-millimeter fabs with more mature process nodes, they basically have a recipe they set and a bunch of technicians looking at machines and looking at the CDs. When the drift happens, they go through their process recipe and manually adjust for the out-of-control processes, and that’s just what they’ve always done. It works for them.”

For these older fabs, however, the repercussions of process drift can be substantial. Minor deviations in process parameters, such as temperature or pressure during the deposition or etching phases, gradually can lead to changes in the physical structure of the semiconductor devices. Over time, these minute alterations can compound, resulting in layers of materials that deviate from their intended characteristics. Such deviations affect critical dimensions and ultimately can compromise the electrical performance of the chip, leading to slower processing speeds, higher power consumption, or outright device failure.

The reliability equation is equally impacted by process drift. Chips are expected to operate consistently over extended periods, often under a range of environmental conditions. However, when process-induced variability can weaken the device’s resilience, precipitating early wear-out mechanisms and reducing its lifetime. In situations where dependability is non-negotiable, such as in automotive or medical applications, those variations can have dire consequences.

But with hundreds of process steps for a typical IC, eliminating all variability in fabs is simply not feasible.

“Process drift is never going to not happen, because the processes are going to have some sort of side effect,” Park said. “The machines go out of spec and things like pumps and valves and all sorts of things need to be replaced. You’re still going to have preventive maintenance (PM). But if the critical dimensions are being managed correctly, which is typically what triggers the drift, you can go a longer period of time between cleanings or the scheduled PMs and get more capacity.”

Process drift pitfalls
Managing process drift in semiconductor manufacturing presents several complex challenges. Hysteresis, for example, is a phenomenon where the output of a process varies not solely because of current input conditions, but also based on the history of the states through which the process already has passed. This memory effect can significantly complicate precision control, as materials and equipment might not reset to a baseline state after each operational cycle. Consequently, adjustments that were effective in previous cycles may not yield the same outcomes due to accumulated discrepancies.

One common cause of hysteresis is thermal cycling, where repeated heating and cooling create mechanical stresses. Those stresses can be additive, releasing inconsistently based on temperature history.  That, in turn, can lead to permanent changes in the output of a circuit, such as a voltage reference, which affects its precision and stability.

In many field-effect transistors (FETs), hysteresis also can occur due to charge trapping. This happens when charges are captured in ‘trap states’ within the semiconductor material or at the interface with another material, such as an oxide layer. The trapped charges then can modulate the threshold voltage of the device over time and under different electrical biases, potentially leading to operational instability and variability in device performance.

Human factors also play a critical role in process drift, with errors stemming from incorrect settings adjustments, mishandling of materials, misinterpretation of operational data, or delayed responses to process anomalies. Such errors, though often minor, can lead to substantial variations in manufacturing processes, impacting the consistency and reliability of semiconductor devices.

“Once in production, the biggest source of variability is human error or inconsistency during maintenance,” said Russell Dover, general manager of service product line at Lam Research. “Wet clean optimization (WCO) and machine learning through equipment intelligence solutions can help address this.”

The integration of new equipment into existing production lines introduces additional complexities. New machinery often features increased speed, throughput, and tighter tolerances, but it must be integrated thoughtfully to maintain the stringent specifications required by existing product lines. This is primarily because the specifications and performance metrics of legacy chips have been long established and are deeply integrated into various applications with pre-existing datasheets.

“From an equipment supplier perspective, we focus on tool matching,” said Dover. “That includes manufacturing and installing tools to be identical within specification, ensuring they are set up and running identically — and then bringing to bear systems, tooling, software and domain knowledge to ensure they are maintained and remain as identical as possible.”

The inherent variability of new equipment, even those with advanced capabilities, requires careful calibration and standardization.

“Some equipment, like transmission electron microscopes, are incredibly powerful,” said Jian-Min Zuo, a materials science and engineering professor at the University of Illinois’ Grainger College of Engineering. “But they are also very finicky, depending on how you tune the machine. How you set it up under specific conditions may vary slightly every time. So there are a number of things that can be done when you try to standardize those procedures, and also standardize the equipment. One example is to generate a curate, like a certain type of test case, where you can collect data from different settings and make sure you’re taking into account the variability in the instruments.”

Process drift solutions
As semiconductor manufacturers grapple with the complexities of process drift, a diverse array of strategies and tools has emerged to address the problem. Advanced process control (APC) systems equipped with real-time monitoring capabilities can extract patterns and predictive insights from massive data sets gathered from various sensors throughout the manufacturing process.

By understanding the relationships between different process variables, APC can predict potential deviations before they result in defects. This predictive capability enables the system to make autonomous adjustments to process parameters in real-time, ensuring that each process step remains within the defined control limits. Essentially, APC acts as a dynamic feedback mechanism that continuously fine-tunes the production process.

Fig. 1: Reduced process drift with AI/ML advanced process control. Source: Tignis

While APC proactively manages and optimizes the process to prevent deviations, fault detection and classification (FDC) reacts to deviations by detecting and classifying any faults that still occur.

FDC data serves as an advanced early-warning system. This system monitors the myriad parameters and signals during the chip fabrication process, rapidly detecting any variances that could indicate a malfunction or defect in the production line. The classification component of FDC is particularly crucial, as it does more than just flag potential issues. It categorizes each detected fault based on its characteristics and probable causes, vastly simplifying the trouble-shooting process. This allows engineers to swiftly pinpoint the type of intervention needed, whether it’s recalibrating instruments, altering processing recipes, or conducting maintenance repairs.

Statistical process control (SPC) is primarily focused on monitoring and controlling process variations using statistical methods to ensure the process operates efficiently and produces output that meets quality standards. SPC involves plotting data in real-time against control limits on control charts, which are statistically determined to represent the expected normal process behavior. When process measurements stray outside these control limits, it signals that the process may be out of control due to special causes of variation, requiring investigation and correction. SPC is inherently proactive and preventive, aiming to detect potential problems before they result in product defects.

“Statistical process control (SPC) has been a fundamental methodology for the semiconductor industry almost from its very foundation, as there are two core factors supporting the need,” said Dover. “The first is the need for consistent quality, meaning every product needs to be as near identical as possible, and second, the very high manufacturing volume of chips produced creates an excellent workspace for statistical techniques.”

While SPC, FDC, and APC might seem to serve different purposes, they are deeply interconnected. SPC provides the baseline by monitoring process stability and quality over time, setting the stage for effective process control. FDC complements SPC by providing the tools to quickly detect and address anomalies and faults that occur despite the preventive measures put in place by SPC. APC takes insights from both SPC and FDC to adjust process parameters proactively, not just to correct deviations but also to optimize process performance continually.

Despite their benefits, integrating SPC, FDC and APC systems into existing semiconductor manufacturing environments can pose challenges. These systems require extensive configuration and tuning to adapt to specific manufacturing conditions and to interface effectively with other process control systems. Additionally, the success of these systems depends on the quality and granularity of the data they receive, necessitating high-fidelity sensors and a robust data management infrastructure.

“For SPC to be effective you need tight control limits,” adds Dover. “A common trap in the world of SPC is to keep adding control charts (by adding new signals or statistics) during a process ramp, or maybe inheriting old practices from prior nodes without validating their relevance. The result can be millions of control charts running in parallel. It is not a stretch to state that if you are managing a million control charts you are not really controlling much, as it is humanly impossible to synthesize and react to a million control charts on a daily basis.”

This is where AI/ML becomes invaluable, because it can monitor the performance and sustainability of the new equipment more efficiently than traditional methods. By analyzing data from the new machinery, AI/ML can confirm observations, such as reduced accumulation, allowing for adjustments to preventive maintenance schedules that differ from older equipment. This capability not only helps in maintaining the new equipment more effectively but also in optimizing the manufacturing process to take full advantage of the technological upgrades.

AI/ML also facilitate a smoother transition when integrating new equipment, particularly in scenarios involving ‘copy exact’ processes where the goal is to replicate production conditions across different equipment setups. AI and ML can analyze the specific outputs and performance variations of the new equipment compared to the established systems, reducing the time and effort required to achieve optimal settings while ensuring that the new machinery enhances production without compromising the quality and reliability of the legacy chips being produced.

AI/ML
Being more proactive in identifying drift and adjusting parameters in real-time is a necessity. With a very accurate model of the process, you can tune your recipe to minimize that variability and improve both quality and yield.

“The ability to quickly visualize a month’s worth of data in seconds, and be able to look at windows of time, is a huge cost savings because it’s a lot more involved to get data for the technicians or their process engineers to try and figure out what’s wrong,” said Park. “AI/ML has a twofold effect, where you have fewer false alarms, and just fewer alarms in general. So you’re not wasting time looking at things that you shouldn’t have to look at in the first place. But when you do find issues, AI/ML can help you get to the root cause in the diagnostics associated with that much more quickly.”

When there is a real alert, AI/ML offers the ability to correlate multiple parameters and inputs that are driving that alert.

“Traditional process control systems monitor each parameter separately or perform multivariate analysis for key parameters that require significant effort from fab engineers,” adds Jain. “With the amount of fab data scaling exponentially, it is becoming humanly impossible to extract all the actionable insights from the data. Machine learning and artificial intelligence can handle big data generated within a fab to provide effective process control with minimal oversight.”

AI/ML also can look for more other ways of predicting when the drift is going to take your process out of specification. Those correlations can be bivariate and multivariate, as well as univariate. And a machine learning engine that is able to sift through tremendous amounts of data and a larger number of variables than most humans also can turn up some interesting correlations.

“Another benefit of AI/ML is troubleshooting when something does trigger an alarm or alert,” adds Park. “You’ve got SPC and FDC that people are using, and a lot of them have false positives, or false alerts. In some cases, it’s as high as 40% of the alerts that you get are not relevant for what you’re doing. This is where AI/ML becomes vital. It’s never going to take false alerts to zero, but it can significantly reduce the amount of false alerts that you have.”

Engaging with these modern drift solutions, such as AI/ML-based systems, is not mere adherence to industry trends but an essential step towards sustainable semiconductor production. Going beyond the mere mitigation of process drift, these technologies empower manufacturers to optimize operations and maintain the consistency of critical dimensions, allowed by the intelligent analysis of extensive data and automation of complex control processes.

Conclusion
Monitoring process drift is essential for maintaining quality of the device being manufactured, but it also can ensure that the entire fabrication lifecycle operates at peak efficiency. Detecting and managing process drift is a significant challenge in volume production because these variables can be subtle and may compound over time. This makes identifying the root cause of any drift difficult, particularly when measurements are only taken at the end of the production process.

Combating these challenges requires a vigilant approach to process control, regular equipment servicing, and the implementation of AI/ML algorithms that can assist in predicting and correcting for drift. In addition, fostering a culture of continuous improvement and technological adaptation is crucial. Manufacturers must embrace a mindset that prioritizes not only reactive measures, but also proactive strategies to anticipate and mitigate process drift before it affects the production line. This includes training personnel to handle new technologies effectively and to understand the dynamics of process control deeply. Such education enables staff to better recognize early signs of drift and respond swiftly and accurately.

Moreover, the integration of comprehensive data analytics platforms can revolutionize how fabs monitor and analyze the vast amounts of data they generate. These platforms can aggregate data from multiple sources, providing a holistic view of the manufacturing process that is not possible with isolated measurements. With these insights, engineers can refine their process models, enhance predictive maintenance schedules, and optimize the entire production flow to reduce waste and improve yields.

Related Reading
Tackling Variability With AI-Based Process Control
How AI in advanced process control reduces equipment variability and corrects for process drift.

The post Predicting And Preventing Process Drift appeared first on Semiconductor Engineering.

A Peoria, Illinois police department tried to recruit new officers with a Call of Duty-inspired campaign on social media, and it was as tone-deaf as you’d imagine. The post, originally shared on the Peoria Police Department’s social media page, showed three white men posing with guns while wearing tactical gear. “Stop…

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