The first part of the project aimed at developing memory devices for in-memory computing. The goal was to explore the concept of projected memory devices and in particular projected phase-change memory devices where the read and write operations are decoupled by exploiting the nonlinear electrical transport in amorphous phase change materials.
As part of this study, we explored various phase-change materials and projection materials. The most important invention in this front was that of single elemental phase-change materials. We showed how the simplest material imaginable, a single element (in this case, antimony), can become a valid alternative when confined in extremely small volumes. This work appeared on the cover of Nature Materials (August 2018 issue) [1]. We also developed the most comprehensive understanding of resistance drift in phase-change materials, the key attribute that we are trying to counter through the concept of projection. This work was published in Advanced Electronics Materials [2]. More recently, we could experimentally measure the onset of structural relaxation in the melt-quenched amorphous phase [3]. We also established a comprehensive understanding of projected phase change memory [4] and also demonstrated the ability to achieve 8-bit equivalent precision arithmetic operations [5]. The latter work was selected as a highlight paper at IEDM.
In the second part of the project, we explored in-memory computing. We showed several applications where we could exploit the physical attributes and state dynamics of phase-change memory devices to perform computation in place. In one application, we showed how we could perform compressed sensing and recovery. This work was published in IEEE Transactions on Electron Devices [6]. We also demonstrated yet another application where we exploit the crystallization dynamics of phase-change memory devices to solve an unsupervised learning algorithm entirely in the memory [7]. A significant challenge for computational memory is the lack of precision associated with it. We proposed a new concept called mixed precision in-memory computing to address this. We used this concept to solve systems of linear equations and this work appeared in Nature Electronics [8]. The mixed-precision in-memory computing concept was also extended to tackle the challenging problem of training deep neural networks [9]. We also developed a multi-PCM device architecture to counter some of the short comings of in-memory computing [10]. We also explored ways to train networks to be resilient to analog in-memory computing [11]. We also explored other applications such as in-memory hyperdimensional computing [12]. Finally, we wrote a couple of tutorials and review articles that have gained a lot of attention in recent years [13,14]
[1] Salinga, Kersting et al., Nature Materials, 2018 (Cover story)
[2] Le Gallo et al., Adv. Electr. Materials, 2018
[3] Kersting et al., Adv. Func. Mat., 2021
[4] Kersting et al., Scientific Reports, 2020
[5] Giannopoulos et al., Proc. IEDM, 2018 (Highlight paper)
[6] Le Gallo et al., IEEE TED., 2018
[7] Sebastian et al., Nature Comm., 2017
[8] Le Gallo et al., Nature Electronics, 2018
[9] Nandakumar et al., Front. Neuroscience, 2020
[10] Boybat et al., Nature Comm., 2018
[11] Joshi et al., Nature Comm., 2020
[12] Karunaratne et al., Nature Electronics, 2020 (Cover story)
[13] Sebastian et al, J. Appl. Phys., 2018
[14] Sebastian et al., Nature Nanotech., 2020 (Cover story)
