Beyond AI Project“Next-generation AI Devices Learned from Biological Fluctuations forRealizing Ultra-low Electric Power Consumption”
Project Leader
Hitoshi Tabata (Professor, School of Engineering, The University of Tokyo )In order to develop devices that utilize fluctuations (noise/heat) in the environment, we are conducting
research in various areas on theoretical calculations, material science, and device design. Solving NP-hardcombinatorial optimization problems (COPs) such as the traveling salesman problem and the knapsackproblem is expected to applied in a wide area not only communication services but also logistics, financialservices, manufacturing processes, etc. An Ising machine is a computer specialized to solve the COPs, wherethe problems are converted into the interactions between spins and the solutions are obtained by searchingfor the ground state (minimum energy). In order to reach the true solution for the Ising machine, noise is used
to escape from local energy minimum. Gaussian white noise is generally used to imitate random processes
in nature, but colored noise (power spectral density: PS=fβ) exists in actual circuits and environments. We
input various types of colored noise into the Ising machine and find the best noise promoting the developmentof the Ising Hamiltonian toward an optimal solution (Fig.1). Because the injection of red noise (β = −2) caneffectively suppress random errors switching spin states and induce stochastic resonance, the optimal solution
was efficiently found even under conditions with large noise. This research proposes improving theperformance of Ising machines by effectively utilizing noise, which has traditionally been considered as anunwanted nuisance. It is expected to be applied for ultra-low energy consumption computers supported bythe environment. Furthermore, we have proposed system models that efficiently utilizes noise such as multistable and over-damped systems, which improve the correct answer rate for hand-written digit patterns related
to image recognition and associative memory, and reduce cost toward short time calculation and low power
consumption.
Regarding materials, we have studied on spin glasses, which have many metastable states in free energy andspin fluctuations. Due to the magnetic interaction with randomness and frustration, spin glasses show a spin
freezing state at low temperatures and exhibit a characteristic aging memory effect recording magnetic history.
Reservoir computing (RC) is one of the machine learning frameworks suitable for time-series data processing
represented by pattern recognition and enables high-speed learning. The learning ability depends on thenonlinearity and short-term memory (STM) capacity of the system. In terms of physical implementation,
spintronic RCs has attracted attention because of the nonvolatile memory, small size, and low power
consumption. Spin glasses are expected to exhibit excellent brain-type functionalities (short-term memorycapacity) due to the slow magnetic dynamics, thus we quantitatively evaluated the STM capacity of spinglasses based on prevalent benchmarks. The results revealed that Co,Si-substituted Lu3Fe5O12 thin films
exhibiting spin glass behavior show superior STM capacity compared to unsubstituted ferrimagnetic thinfilms (Fig.2). STM performance has been improved reflecting the time constant of magnetic relaxation
reaching maximum near the spin freezing temperature, thus spin glass can be considered as possible
candidates for RC with better performance. We also reported on the development of ultra-low power
consumption devices using spin waves as an information media without heat loss. We demonstrated areconfigurable logic gates, which is constructed by the garnet-type ferrimagnetic iron oxide Y3Fe5O12 withhighly efficient spin wave propagation, using spin wave interference between magnetostatic surface waves
(a)

(b)

(c)

M (a.u.) H (Oe)Binarycodes

(b)

(a)
(a)

1(b)

0100

-6501

FC

00(c)
STM capacity

ZFC5001000 1500 2000
Sampling points (time, a.u.)

Spinglass (Tg~200K)
1.0

Ferrimagnet

Interference Gain (dB)(a)-70-75-80-90-9500K
1K
2K
3K
4K
5K
6K-85

0

10K 150K 300K 10K 150K 300K

60120180240
phase (degree)

300

360

Temperatureand backward volume waves in addition to local external field control (Fig.3). We also demonstrated that,
unlike conventional garnet-type oxides, spinel-type oxides, which have a relatively simple crystal
structure and can be used for heterodevice applications, can propagate spin waves with highefficiency.
Selected Publications:
1.

Z. Liao, K. Ma, Md S. Sarker, S. Tang, H. Yamahara, M. Seki, and H. Tabata, “Quantum Analog Annealing of
Gain-Dissipative Ising Machine Driven by Colored Gaussian Noise”, Adv. Theory Simul. 2100497 (2021).

2.

Z. Shia, Z. Liao, H. Tabata, “Boosting learning ability of overdamped bistable stochastic resonance system
based physical reservoir computing model by time-delayed feedback”, Chaos Solitons Fractals, 161, 112314(2022).

3.

Z. Liao, H. Yamahara, K. Terao, K. Ma, M. Seki, H. Tabata, “Short‑term memory capacity analysis of
Lu3Fe4Co0.5Si0.5O12‑based spin cluster glass towards reservoir computing”, Sci. Rep. 13, 5260 (2023).

4.

Md S. Sarker, H. Yamahara, L. Yao, S. Tang, Z. Liao, M. Seki, H. Tabata, “Sensitivity enhancement inmagnetic sensor using CoFeB/Y3Fe5O12 resonator”, Sci. Rep. 12, 11105 (2023).

5.

Md S. Sarker, L. Yao, H. Yamahara, K. Ma, Z. Liao, K. Terao, S. Tang, S. G. Ramaraj, M. Seki, H. Tabata,“Reconfigurable magnon interference by on‑chip dynamic wavelength conversion”, Sci. Rep. 13, 4872
(2023).

6.

S. Tang, MD S. Sarker, K. Ma, H. Yamahara, H. Tabata, M. Seki, “Efficient spin-wave transmission inepitaxial thin films of defect spinel γ-Fe2−xAlxO3”, Appl. Phys. Lett. 119, 082402 (2021).

Spin-waves, the collective excitations of electron spins propagating through low-damping magneticmaterials, are considered to be a promising avenue for transmitting information without generatingheat. This property makes them an ideal foundation for the development of a next-generation
computing with ultra-low power consumption. In the previous researches, to detected theinterference gain of the spin-wave device, which required two signals in different phases to beprovided to the two ports respectively, an external vector network analyzer (VNA) system is usuallyused. The use of such a system is both costly and bulky, making it impractical for portable integration.
The primary goal of our research is to realize a highly power-efficient integrated device based on
spin fluctuation. To achieve this goal we are developing a high-sensitivity interface circuit for weaksignal detection from spin-glass devices. Figure 1 illustrates a block diagram of the spin-wave
detection circuit system, which is composed of a phase-locked loop as a sinewave signal generator,

a phase interpolator to tune the relative phase of the two stimulus signal to the spin-wave device, alow-noise amplifier to amplify the weak signal from the device, mixers to down-convert the signal
frequency, and baseband amplifiers and filters to acquire the signal to be processed by a subsequent
digital processor. We have developed these building blocks to compose the system and fabricated achip for verification as shown in Fig. 2. Among them, a chip photo of the low-noise inductorless
PLL with a cascaded architecture is shown in Fig. 3. This PLL features extremely low jitter and spurwithout using an on-chip inductor, which is sensitive to the external magnetic field. In addition, wehave applied stochastic resonance, where the noise helps to improve the signal-to-noise ratio ofnonlinear systems, to enhance the performance of the analog-to-digital converters. The analysis
proves the meaningful performance improvement with proper intensity of noise as shown in Fig. 4.

Fig. 1. Block diagram of spin-wave detection circuit
210μmPower Consumption
1.25GHz Ring VCOIntegralCap.

Pulse Gen.

280μm

GM S/H
Test
BufferRing
VCO

RingVCO

Pulse Gen.
PLL1
(59%) VCO Buf. + Slope Gen. + S/H

Slope
Gen.

S/H

Slope
Gen.

VCO
Buffer DIV4

VCO
Buffer

PLL1

GM

PLL2

IntegralCap.

Test
BufferFig. 2. Chip photo of the system

PLL2
(41%)

46.7%
4.4%
7.6%

GM

0.4%

10GHz Ring VCO

23.8%

DIV4 + Slope Gen. + S/H

9.9%

VCO Buffer

7.0%

GM

0.4%

Total Power: 30.82mW

Fig. 3. Chip photo of inductorless PLL

Fig. 4. ADC Performance enhancement

Selected Publications:
1.

R. Shibata, Y. Hotta, H. Tabata and T. Iizuka, “Analysis of SAR ADC Performance Enhancement utilizing
Stochastic Resonance,” IEEE Trans. on Circ. and Syst.-II: Express Briefs, 2023.

2.

Y. Zhu, Z. Yang, Z. Cheng, Md S. Sarker, H. Yamahara, M. Seki, H. Tabata and T. Iizuka, “A 1-5GHz
Inverter-Based Phase Interpolator With All Digital Control for Spin-Wave Detection Circuit,” IEEE ICICDT,Sep. 2023.

3.

Z. Cheng, Z. Yang, Y. Zhu, Md S. Sarker, H. Yamahara, M. Seki, H. Tabata and T. Iizuka, “Design of 1-5
GHz Two-Stage Noise-Canceling Low-Noise Amplifier With Gm-Boosting Technique for Spin Wave
Detection Circuit,” IEEE ICICDT, Sep. 2023.

4.

Z. Yang, Z. Xu, M. Osada and T. Iizuka, “A 10-GHz Inductorless Cascaded PLL with Zero-ISF Subsampling
Phase Detector Achieving −63-dBc Reference Spur, 175-fs RMS Jitter and −240-dB FOMjitter,” IEEESymp. VLSI Technology and Circuits, Jun. 2022.

Design-driven exploration of spin glass materials with high spin-freezing temperatures requires (i)description of electronic structure in strongly correlated electron systems, (ii) treatment of disordersystems, and (iii) combination of first-principle calculations and statistical mechanics models. We
have developed a fundamental simulation method and software to satisfy the above requirements.
The software is based on the KKR Green's function method, and by using the self-interaction
correction method, it is possible to describe the electronic structure of strongly correlated materials,
which has been considered difficult, without any empirical parameters. The spin glass phenomenon
is caused by the atomic randomness and magnetic frustration. The atomic randomness can betreated by the coherent potential approximation, and the magnetic frustration can be quantitatively
evaluated by calculating magnetic interactions and effective pair interactions between the magnetic

atoms. Finally, combining the above results with the replica exchange Monte Carlo method, we canestimate the spin-freezing temperature of the spin glass materials with high accuracy. Figure 1
shows a schematic of the software we have developed. Figure 2 indicates the spin-freezing
temperatures of CuMn alloys calculated by our software, where the experimental results are wellreproduced and the validity of our method is proven. Figure 3 shows the chemical trend of the spinfreezing temperature for magnetite-based spin glass materials. From these results, we succeeded in
obtaining a guideline for achieving high spin-freezing temperatures. Experiments are currently
conducted according to this guideline. We are also planning to perform an exhaustive search bycombining this software with a supercomputer.
Scale-bridging simulator(a) AkaiKKR(b ) Sp in interactio nNo d etection（ b elow 80 0 K）

(c) Ato m ic interactio n(d )M o nte Carlo sim ulatio n

Fig.1 Scale-bridging simulator. (a)Fig.2 Spin freezing tempera

Fig.3 Chemical trend for t

AkaiKKR code, (b) magnetic excha

ture of Mn 17 doped FCC-

he spin freezing temperatu

nge interaction, (c) effecitive pair iCu.

re of Ti doped magnetite

nteraction, and (d) Monte Carlo mFe3O4.

ethod.

Selected publications:
1. T. Fukushima, H. Akai, T. Chikyow, and H. Kino, “Automatic exhaustive calculations of largematerial space by Korringa-Kohn-Rostoker coherent potential approximation method — Applied toequiatomic quaternary high entropy alloys”, Physical Review Materials 6 023802/1-19 (2022).
2. T. Fukushima, H. Akai, T. Chikyo, and H. Kino, “Automatic Generation of Material Property
Data by Supercomputer "Fugaku" - Toward Realization of Big Data in Magnetic Materials, February
16th, 2022