Thursday, December 30, 2021

Interview with Maksym Plakhotnyuk, CEO & Co-Founder, Atlanta 3D Nanosystems

Here an insightful interview with Maksym Plakhotnyuk, CEO & Co-Founder, Atlanta 3D Nanosystems on Advancements in Micro & Nano Electronics – What it Means for the Semiconductor Industry

ATLANT 3D Nanosystems is a cross-European deep tech company that developed a unique atomic layer advanced manufacturing technology (direct write ALD) with a mission to reshape the future atom by atom and enable on-demand advanced materials development, rapid prototyping and manufacturing of microdevices and nanodevices. ATLANT 3D team is highly dynamic, international, and multidisciplinary, consisting of 15 experienced entrepreneurs, engineers and scientists. ATLANT 3D collaborates with leading European universities, such as SAS, DTU, FAU, TNO, SUPSI and industrial partners such as Merck, ST Microelectronics, Prima Industrie and SEMPA.

Wednesday, December 15, 2021

Redstone’s North Karelia Growth Fund makes a pre-seed investment in Chipmetrics, a spin-off of VTT Finland

Redstone’s North Karelia Growth Fund makes a pre-seed investment in Chipmetrics, a spin-off of VTT. Chipmetrics is based in Joensuu, Finland, one of the leading photonics hubs in Europe. The company is a forerunner in productizing test structures, test chips, and related measurement concepts for advanced materials and microelectronics manufacturing industries.

The 3D nanometrology startup is part of the emerging Atomic Layer Deposition industry and research community. ALD is a key enabler of the 3D megatrend in the semiconductor industry by improving the performance and energy efficiency of transistors and memory circuits.

We wish CEO Mikko Utriainen and the whole team the best of success in this sizable global industry and are delighted to support the venture.

Chipmetrics will launch a seed round in 2022. We are looking forward to seeing you grow!

Tuesday, December 14, 2021

2022 Atomic Layer Processing Modelling Workshop

Pedersen group is organizing a work shop on modelling of atomic layer processes in Linköping 15-16 March next year with a Tutorial by Ray Adomaitis. Possibility to join via Zoom. 

Welcome to a forum where experimentalists and modellers from academia and industry meet to collaboratively push the boundaries of multi-scale modelling.

Predict the Future of Thin Films

Is complete in silico development of new materials and methods a utopia or just around the corner? Join us on-site or online and discuss state-of-the-art scientific methods to model atomic layer processes such as CVD, ALD and ALE, from reactorscale to atomic level.

The conference will have a mix of contributed talks, describing the latest in ALP modelling (atomic layer processing) and industry lectures presenting areas that need modelling. 


Prof. Raymond Adomaitis, from University of Maryland, will describe his way of modelling ALP in the tutorial “Reaction network analysis of ALD processes: Is this a true ALD cycle? What rates can be measured?”.


Date and time: March 15-16, starting on Tuesday at 12.00. 

Place: Planck, Fysikhuset. You will also be able to participate online via link.

Abstract deadline: February 2022.

The conference is free of charge but to participate you will have to register. Registration and Abstract aplication will soon be available here.

Wednesday, December 8, 2021

Meaglow Hollow Cathode Gas Plasma Source Paper Published by the Journal “Coatings”

Meaglow technical staff have published a paper entitled: “Recent Advances in Hollow Cathode Technology for Plasma-Enhanced ALD — Plasma Surface Modifications for Aluminum and Stainless-Steel Cathodes” in the journal “Coatings”. An early version of the paper can be accessed at the journal website, here.

The paper provides a brief review of oxygen contamination from ICP and microwave legacy sources, but also provides details of the operation of the hollow cathode gas plasma sources now widely used by the ALD community.

Tuesday, December 7, 2021

How Machine Learning Enables Accurate Prediction of Precursor Volatility

by Simon Elliott, Director of Atomic Level Process Simulation, Schrödinger

Challenges in predicting volatility

A crucial process in manufacturing CPUs and other high-tech devices is the deposition of solid material from reactive vapors. Different precursor vapors are used for chemical vapor deposition, vapor phase epitaxy, atomic layer deposition – and indeed the reverse process of atomic layer etching – with the precursor chemistry carefully designed for each case so as to control material quality at the nanoscale. But what all these techniques have in common is that the precursor chemicals must evaporate or sublime at a low enough temperature. Too much heating when vaporizing a precursor can make it decompose, causing it to be undeliverable to the growing surface.

With volatility playing such a central role in this technology (and in other fields like distillation, refrigeration, inkjet printing, food, and perfumes), it is surprising that we understand so little about it. Volatility is the product of a remarkably fine balance of interatomic forces, dictating the extent to which molecules condense together as a solid or liquid, or bounce apart into a vapor and deliver a certain vapor pressure at any given temperature. These interatomic forces can be computed very precisely with quantum mechanics for one molecule or a group of molecules, but not at the scale of a liquid or solid. Even with today’s computing power, routinely and accurately predicting precursor volatility ‘from first principles’ remains unfortunately out of reach.

Machine learning approaches

Could an alternative more empirical approach prove useful? Does enough experimental data exist to find the relation between volatility and chemical structure? The vaporization of some organic molecules, such as alcoholic fractions or natural fragrances, has been of interest for centuries and high-quality vapor pressure data are available in the literature. Over the last decade, these data have been analyzed with advanced fitting algorithms that come under the umbrella of ‘machine learning’. Schrödinger has leveraged the latest machine learning techniques to develop a highly accurate model that predicts the volatility of organic molecules up to C20.

However, when building machine learning models to predict volatility of precursor molecules, which are typically organometallic complexes, the situation is not so straightforward. New precursor molecules are constantly being proposed and evaluated. Commercial sensitivity sometimes means that data are partially withheld or plagued by experimental configuration differences from laboratory to laboratory. Additionally, for the common aim of material processing, complete pressure-temperature curves are rarely measured, as it is more pragmatic to focus on the temperature for vapor to transport successfully to the reactor. As a result, datasets for building predictive models are sparse and incomplete.

Prediction of volatility for inorganic and organometallic complexes 

Schrödinger scientists embarked on the challenge of building machine learning models to predict the volatility of precursor molecules. Using in-house expertise in machine learning and advanced informatics, Schrödinger scientists collated and digitized information about organometallic precursors from disparate literature sources and applied a variety of machine learning algorithms (such as Random Forest and Neural Networks) in conjunction with different chemoinformatic descriptors and fingerprints. The result is the first capability of its kind for accurately and efficiently predicting the volatility for inorganic and organometallic complexes from their chemical structures. For complexes of the fifty most common metals and semimetals, the model predicts the evaporation or sublimation temperature at a given vapor pressure with an average accuracy of ±9°C (which is about 3% of the absolute temperature). As a trained model, the turnaround time is fast with the ability to compute hundreds of complexes per second.

New avenues for precursor development

This predictive model opens a new path for designing novel precursors with improved performance, not only improving their deposition or etch chemistry, but also optimizing the temperature at which they evaporate or sublime and can be delivered as a vapor. This advance will allow a much wider range of structural modifications to be screened computationally than before and will produce candidate precursors for experimental synthesis and testing that are both less risky and more innovative. This volatility model, together with Schrödinger’s quantum mechanics-based workflows for computation of reactivity and decomposition, gives scientists a complete design kit for vapor-phase deposition or etch, delivering a faster pace of research into materials and processes for new technologies.

* The banner image is from Tyndall National Institute.

About the author

Dr. Simon Elliott is Director of Atomic Level Process Simulation at Schrödinger. From 2001-2018 he led a research group at Tyndall National Institute, Ireland. Prior to that, he studied chemistry at Trinity College Dublin and Karlsruhe Institute of Technology. He qualified as a Project Management Professional and is a Fellow of the Royal Society of Chemistry. He was co-chair of the 16th International Conference on Atomic Layer Deposition and chair of a 175-member COST network on the same topic.

About Schrödinger

Schrödinger is an industry-leading computational solutions provider for both life science and materials science, with a mission to improve human health and quality of life by transforming the way therapeutics and materials are discovered.

With the goal to accelerate the discovery and optimization of novel materials by a digital chemistry platform governed by physics-based modeling, amplified by machine learning, and optimized through team-based intelligence, Schrödinger’s Materials Science platform offers unprecedented insights into the mechanisms and properties of materials and chemical systems in a wide range of technological applications: Organic Electronics, Polymeric Materials, Consumer Packaged Goods, Catalysis & Reactive Systems, Semiconductors, Energy Capture & Storage, Complex Formulations, Metals, Alloys & Ceramics.

Learn more

Request more information

Beneq unveils two new ALD products for 300mm and compound semiconductor device fabrication respectively

Beneq revolutionized ALD cluster tools for More-than-Moore device makers with the highly successful Beneq Transform® family of products. Today, Beneq broadens its product portfolio further with two new distinct solutions: the Transform® 300 and ProdigyTM.

The Beneq Transform 300 and Prodigy were each created in response to specific technology requirements in the semiconductor manufacturing sector.

“The Transform 300 is designed to meet the growing demand of emerging semiconductor applications at 300 mm for devices such as CMOS image sensors, Power Devices, Micro-OLED/LED, and Advanced Packaging, which call for a high degree of versatility,” explains Patrick Rabinzohn, Vice President, Semiconductor ALD at Beneq.

“We created Prodigy to address those market segments that need a simple solution supported by high-end technology. It inherits the ALD design and processing knowhow we at Beneq have developed over the last 15 years, packing advanced features in a simpler, targeted industrial form factor,” continues Rabinzohn.
Beneq Transform 300 is the only 300 mm ALD cluster tool that combines thermal ALD (batch) and plasma ALD (single wafer) technologies to provide a highly versatile platform for IDMs and foundries. It is dedicated to advanced thin-film applications in CIS, Power, Micro-OLED/LED, Advanced Packaging and other MtM applications.

Beneq Tranform 300 is a highly configurable platform that caters to multiple advanced thin-film applications ranging from gate dielectric including in high aspect ratio trenches, to anti-reflection coating, final passivation or encapsulation, Chip-Scale-Packaging and beyond.

Beneq Prodigy is the deal manufacturing solution for compound semiconductor including RF IC’s (GaAs/GaN/InP), LED, VCSEL, Light Detectors and for MEMS manufacturers and foundries looking to enhance device performance and reliability through an affordable stand-alone ALD batch tool. Beneq Prodigy provides best-of-breed passivation and encapsulation films across multiple wafer types and sizes.

To learn more, visit:

Thursday, December 2, 2021

Master Program in ALD at Helsinki University!

On de­mand tailored train­ing  

We can organise special training on ALD and thin film characterisation. The content can be specifically tailored to meet your specific needs. Contact for further details!
M.Sc. in ALD

Application period for our Master's programme is open! Apply now to study inorganic materials chemistry with a focus on atomic layer deposition.

HelsinkiALD / ALD center Finland is now providing a well thought-out academic education program on Atomic Layer Deposition with skills highly relevant for also other thin film technologies.

The selected students will be majoring from Inorganic materials chemistry study track of Master’s Programme in Materials Research (link), but the courses are directed so that they are covering all important aspects of ALD, including precursors, thin film deposition and characterization as well as potential applications.

As a final part, Master's Thesis in the field of ALD is carried out, either in our HelsinkiALD team or companies working on ALD technology. Upon graduation a special certificate on the ALD focus will be awarded together with the Inorganic materials chemistry diploma.

List of courses for ALD master studies. Optional course selection agreed with the supervisor, with strongest recommendation to courses listed below.

Wednesday, December 1, 2021

Webinar RIE and ALE Processes for Quantum Devices

Optimise the Fabrication Process for Quantum Devices, 2nd December, 4 pm (GMT)

Dr Russ Renzas, Quantum Technology Market Manager

The fabrication of superconducting qubits, quantum photonic elements and diamond-based quantum sensors require highly controlled, stable processes that will not damage the surface and leave no residues. Plasma-based Reactive Ion Etch and Atomic Layer Etch processes are critical for the fabrication of these quantum devices.

In this webinar, Dr Russ Renzas will give an overview of the available plasma etch solutions and how each one of them can be used to overcome some of the processing roadblocks, providing specific examples of how they are applied during the fabrication process

This webinar will give an introduction of:
  • The various etch platforms that are available and their differences
  • How each etch system can be used to overcome some of the processing roadblocks
  • Quantum-specific examples of what can be done and why it should be done during device fabrication process