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1. Temperature-resilient solid-state organic artificial synapses for neuromorphic computing

   https://doi.org/10.1126/sciadv.abb2958  

   Melianas, A. ; Quill, T. J. ; LeCroy, G. ; Tuchman, Y. ; Loo, H. v. ; Keene, S. T. ; Giovannitti, A. ; Lee, H. R. ; Maria, I. P. ; McCulloch, I. ; et al ( July 2020 , Science Advances)

   Devices with tunable resistance are highly sought after for neuromorphic computing. Conventional resistive memories, however, suffer from nonlinear and asymmetric resistance tuning and excessive write noise, degrading artificial neural network (ANN) accelerator performance. Emerging electrochemical random-access memories (ECRAMs) display write linearity, which enables substantially faster ANN training by array programing in parallel. However, state-of-the-art ECRAMs have not yet demonstrated stable and efficient operation at temperatures required for packaged electronic devices (~90°C). Here, we show that (semi)conducting polymers combined with ion gel electrolyte films enable solid-state ECRAMs with stable and nearly temperature-independent operation up to 90°C. These ECRAMs show linear resistance tuning over a >2× dynamic range, 20-nanosecond switching, submicrosecond write-read cycling, low noise, and low-voltage (±1 volt) and low-energy (~80 femtojoules per write) operation combined with excellent endurance (>10 9 write-read operations at 90°C). Demonstration of these high-performance ECRAMs is a fundamental step toward their implementation in hardware ANNs.
2. Transparent InGaZnO-Based Resistive Random Access Memory

   Qin, Fei ; Lee, Sunghwan ( May 2022 , Materials Research Society)

   The major focus of artificial intelligence (AI) research is made on biomimetic synaptic processes that are mimicked by functional memory devices in the computer industry [1]. It is urgent to find a memory technology for suiting with Brain-Inspired Computing to break the von Neumann bottleneck which limits the efficiency of conventional computer architectures [2]. Silicon-based flash memory, which currently dominates the market for data storage devices, is facing challenging issues to meet the needs of future data storage device development due to the limitations, such as high-power consumption, high operation voltage, and low retention capacity [1]. The emerging resistive random-access memory (RRAM) has elicited intense research as its simple sandwiched structure, including top electrode (TE) layer, bottom electrode (BE) layer, and an intermediate resistive switching (RS) layer, can store data using RS phenomenon between the high resistance state (HRS) and the low resistance state (LRS). This class of emerging devices is expected to outperform conventional memory devices [3]. Specifically, the advantages of RRAM include low-voltage operation, short programming time, great cyclic stability, and good scalability [4]. Among the materials for RS layer, indium gallium zinc oxide (IGZO) has attracted attention because of its abundance and high atomic diffusion property ofmore »oxygen atoms, transparency, and its easily modulated electrical properties by controlling the stoichiometric ratio of indium and gallium as well as oxygen potential in the sputter gas [5, 6]. Moreover, since the IGZO can be applied to both the thin-film transistor (TFT) channel and RS layer, the IGZO-based fully integrated transparent electronics are very promising [5]. In this work, we proposed transparent IGZO-based RRAMs. First, we chose ITO to serve as both TE and BE to achieve high transmittance in the visible regime of light. All three layers (TE, RS, BE layers) were deposited using a multi-target magnetron sputtering system on glass substrates to demonstrate fully transparent oxide-based devices. I-V characteristics were evaluated by a semiconductor parameter analyzer, and our devices showed typical butterfly curves indicating the bipolar RS property. And the IGZO-based RRAM can survive more than 50 continuous sweeping cycles. The optical transmission analysis was carried out via an UV-Vis spectrometer and the average transmittance around 80% out of entire devices in the visible-light wavelength range, implying high transparency. To investigate the thickness dependence on the properties of RS layer, 50nm, 100nm and 150nm RS layer of IGZO RRAM were fabricated. Also, the oxygen partial pressure during the sputtering of IGZO was varied to optimize the property because the oxygen vacancy concentration governs the RS and RRAM performance. Electrode selection is crucial and can impact the performance of the whole device [7]. Thus, Cu TE was chosen for our second type of device because the diffusion of Cu ions can be beneficial for the formation the conductive filament (CF). Finally, a ~5 nm SiO2 barrier layer was employed between TE and RS layers to confine the diffusion of Cu into the RS layer. At the same time, this SiO2 inserting layer can provide an additional interfacial series resistance in the device to lower the off current, consequently, improve the on/off ratio and whole performance. In conclusion, the transparent IGZO-based RRAMs were established. To tune the property of RS layer, the thickness layer and sputtering conditions of RS were adjusted. In order to engineer the diffusion capability of the TE material to the RS layer and the BE, a set of TE materials and a barrier layer were integrated in IGZO-based RRAM and the performance was compared. Our encouraging results clearly demonstrate that IGZO is a promising material in RRAM applications and overcoming the bottleneck of current memory technologies.« less
3. Oxygen-bearing functionalities enhancing NO ₂ , NH ₃ , and acetone electronic response and response variation by polythiophenes in organic field-effect transistor sensors

   https://doi.org/10.1039/D1TC04650K  

   Wagner, Justine ; Song, Yunjia ; Shapiro, Jenna ; Katz, Howard E. ( February 2022 , Journal of Materials Chemistry C)

   We investigated the enhanced vapor responses and altered response ratios of a series of thiophene (co)polymers with oxygenated side chains (CH 2 OH, linear polyethylene glycol, and crown ether), including the novel poly(3-hydroxymethylthiophene) (PTOH) and other newly synthesized polymers. Hydroxymethyl-containing copolymers had higher mobility compared to poly(3-hexylthiophene) (P3HT). The larger crown ether moiety promotes transistor characteristics of P3HT while the smaller one impairs them. Incorporating different oxygen bearing functionalities increased responses of thiophene polymers to NO 2 , NH 3 , and acetone. For example a polyether side chain increases the NO 2 response sensitivity of copolymers of both P3HT and PTOH, but sensitivity towards gas analytes was more prominent for glycol-based functionalities rather than the crown ethers. PTOH is very sensitive to NO 2 and the response likely includes a contribution from conductive protons on the OH group. The lack of correlation among the rank-ordered gas sensitivities imparted by each functional group was found to be useful for designing a selective sensor array. We specifically showed high classification accuracy for all the polymer responses to NO 2 and acetone vapors, both of which gave increased device currents but with response ratios different enough to allow highly classifying discriminant functionsmore »to be derived.« less
4. (Digital Presentation) Investigation of Top Electrodes Impact on Performance of Transparent Amorphous Indium Gallium Zinc Oxide (a-InGaZnO) Based Resistive Random Access Memory

   https://doi.org/10.1149/MA2022-01191075mtgabs  

   Qin, Fei ; Lee, Sunghwan ( July 2022 , ECS Meeting Abstracts)

   The traditional von Neumann architecture limits the increase in computing efficiency and results in massive power consumption in modern computers due to the separation of storage and processing units. The novel neuromorphic computation system, an in-memory computing architecture with low power consumption, is aimed to break the bottleneck and meet the needs of the next generation of artificial intelligence (AI) systems. Thus, it is urgent to find a memory technology to implement the neuromorphic computing nanosystem. Nowadays, the silicon-based flash memory dominates non-volatile memory market, however, it is facing challenging issues to achieve the requirements of future data storage device development due to the drawbacks, such as scaling issue, relatively slow operation speed, and high voltage for program/erase operations. The emerging resistive random-access memory (RRAM) has prompted extensive research as its simple two-terminal structure, including top electrode (TE) layer, bottom electrode (BE) layer, and an intermediate resistive switching (RS) layer. It can utilize a temporary and reversible dielectric breakdown to cause the RS phenomenon between the high resistance state (HRS) and the low resistance state (LRS). RRAM is expected to outperform conventional memory device with the advantages, notably its low-voltage operation, short programming time, great cyclic stability, and good scalability.more »Among the materials for RS layer, indium gallium zinc oxide (IGZO) has shown attractive prospects in abundance and high atomic diffusion property of oxygen atoms, transparency. Additionally, its electrical properties can be easily modulated by controlling the stoichiometric ratio of indium and gallium as well as oxygen potential in the sputter gas. Moreover, since the IGZO can be applied to both the thin-film transistor (TFT) channel and RS layer, it has a great potential for fully integrated transparent electronics application. In this work, we proposed amorphous transparent IGZO-based RRAMs and investigated switching behaviors of the memory cells prepared with different top electrodes. First, ITO was choosing to serve as both TE and BE to achieve high transmittance. A multi-target magnetron sputtering system was employed to deposit all three layers (TE, RS, BE layers) on glass substrate. I-V characteristics were evaluated by a semiconductor parameter analyzer, and the bipolar RS feature of our RRAM devices was demonstrated by typical butterfly curves. The optical transmission analysis was carried out via a UV-Vis spectrometer and the average transmittance was around 80% out of entire devices in the visible-light wavelength range, implying high transparency. We adjusted the oxygen partial pressure during the sputtering of IGZO to optimize the property because the oxygen vacancy concentration governs the RS performance. Electrode selection is crucial and can impact the performance of the whole device. Thus, Cu TE was chosen for our second type of device because the diffusion of Cu ions can be beneficial for the formation of the conductive filament (CF). A ~5 nm SiO 2 barrier layer was employed between TE and RS layers to confine the diffusion of Cu into the RS layer. At the same time, this SiO 2 inserting layer can provide an additional interfacial series resistance in the device to lower the off current, consequently, improve the on/off ratio and whole performance. Finally, an oxygen affinity metal Ti was selected as the TE for our third type of device because the concentration of the oxygen atoms can be shifted towards the Ti electrode, which provides an oxygengettering activity near the Ti metal. This process may in turn lead to the formation of a sub-stoichiometric region in the neighboring oxide that is believed to be the origin of better performance. In conclusion, the transparent amorphous IGZO-based RRAMs were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of RRAMs, we integrated a set of TE materials, and a barrier layer on IGZO-based RRAM and compared the switch characteristics. Our encouraging results clearly demonstrate that IGZO is a promising material in RRAM applications and breaking the bottleneck of current memory technologies.« less
5. C3SRAM: An In-Memory-Computing SRAM Macro Based on Robust Capacitive Coupling Computing Mechanism

   https://doi.org/10.1109/JSSC.2020.2992886  

   Jiang, Zhewei ; Yin, Shihui ; Seo, Jae-sun ; Seok, Mingoo ( May 2020 , IEEE Journal of Solid-State Circuits)

   This article presents C3SRAM, an in-memory-computing SRAM macro. The macro is an SRAM module with the circuits embedded in bitcells and peripherals to perform hardware acceleration for neural networks with binarized weights and activations. The macro utilizes analog-mixed-signal (AMS) capacitive-coupling computing to evaluate the main computations of binary neural networks, binary-multiply-and-accumulate operations. Without the need to access the stored weights by individual row, the macro asserts all its rows simultaneously and forms an analog voltage at the read bitline node through capacitive voltage division. With one analog-to-digital converter (ADC) per column, the macro realizes fully parallel vector–matrix multiplication in a single cycle. The network type that the macro supports and the computing mechanism it utilizes are determined by the robustness and error tolerance necessary in AMS computing. The C3SRAM macro is prototyped in a 65-nm CMOS. It demonstrates an energy efficiency of 672 TOPS/W and a speed of 1638 GOPS (20.2 TOPS/mm 2 ), achieving 3975 × better energy–delay product than the conventional digital baseline performing the same operation. The macro achieves 98.3% accuracy for MNIST and 85.5% for CIFAR-10, which is among the best in-memory computing works in terms of energy efficiency and inference accuracy tradeoff.